Next Article in Journal
How to Bell the Cat? A Theoretical Review of Generative Artificial Intelligence towards Digital Disruption in All Walks of Life
Previous Article in Journal
Developments and Applications of Artificial Intelligence in Music Education
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Opinion

Matching Assistive Technology, Telerehabilitation, and Virtual Reality to Promote Cognitive Rehabilitation and Communication Skills in Neurological Populations: A Perspective Proposal

1
Law Department, “Giustino Fortunato” University of Benevento, 82100 Benevento, Italy
2
Department of Educational Sciences, Psychology, Communication, Università degli Studi di Bari Aldo Moro, 70121 Bari, Italy
3
Faculty of Management Sciences, Durban University of Technology, Durban 4041, South Africa
*
Author to whom correspondence should be addressed.
Technologies 2023, 11(2), 43; https://doi.org/10.3390/technologies11020043
Submission received: 20 February 2023 / Revised: 9 March 2023 / Accepted: 13 March 2023 / Published: 16 March 2023
(This article belongs to the Section Assistive Technologies)

Abstract

:
Neurological populations (NP) commonly experience several impairments. Beside motor and sensorial delays, communication and intellectual disabilities are included. The COVID-19 pandemic has suddenly exacerbated their clinical conditions due to lockdown, quarantine, and social distancing preventive measures. Healthcare services unavailability has negatively impacted NP clinical conditions, partially mitigated by vaccine diffusion. One way to overcome this issue is the use of technology-aided interventions for both assessment and rehabilitative purposes. Assistive technology-based interventions, telerehabilitation, and virtual reality setups have been widely adopted to help individuals with neurological damages or injuries. Nevertheless, to the best of our knowledge, their matching (i.e., combination or integration) has rarely been investigated. The main objectives of the current position paper were (a) to provide the reader with a perspective proposal on the matching of the three aforementioned technological solutions, (b) to outline a concise background on the use of technology-aided solutions, (c) to argue on the effectiveness and the suitability of technology-mediated programs, and (d) to postulate an integrative proposal to support cognitive rehabilitation including assistive technology, telerehabilitation, and virtual reality. Practical implications for both research and practice are critically discussed.

1. Introduction

The term neurological population (NP) usually refers to any individual who presents an acquired or congenital damage and/or injury to the central nervous system. The autonomic nervous system, brain, cranial nerves, muscles, nerve roots, neuromuscular junctions, peripherical nerves, and spinal cord are usually embedded. Persons with neurological impairments may have different levels of intellectual disabilities, sensorial disorders, motor delays, communication inabilities, and lack of speech. People affected by neurodevelopmental disorders (i.e., attention deficits hyperactivity disorders, autism spectrum disorders, cerebral palsy, rare genetic diseases), neurodegenerative diseases (i.e., Alzheimer’s and Parkinson’s disease, lateral amyotrophic sclerosis, and multiple sclerosis), and/or with acquired brain injuries and stroke, and post-coma patients, either in a vegetative state or in a minimally conscious state, may be included.
The main feature of these categories of patients is the presence of multiple disabilities (ranging from mild–moderate to severe or profound) that has consequences on the level of residual functioning [1,2,3,4,5]. Therefore, persons with neurological impairments necessarily rely on caregivers, families, practitioners, and professionals’ daily assistance [6,7]. Because they are unable to positively tackle daily environmental requests, persons diagnosed with neurological impairments may experience detachment, isolation, passivity, and withdrawal throughout their lifespan as a chronic clinical condition [8,9,10]. Whenever an acute clinical condition occurs, it may seriously hamper their social image and status [11,12]. Thus, it may be deleterious for their quality of life. In fact, their clinical conditions may have negative outcomes on caregivers, professionals, and families’ burden [13,14,15].
AT includes devices, equipment, pieces, or tools capable of ensuring NP independence and self-determination. Based on learning principles (i.e., causal association between behavioral responses and environmental consequences), an AT-based program provides a functional bridge between the limited human repertoire and high environmental requests. Accordingly, an active role, constructive engagement, positive participation, and profitable occupation will be enhanced [16,17,18,19,20,21,22]. Microswitches represent a basic form of AT-based interventions and usually include electronic sensors and tools capable of detecting small behavioral responses. Consequently, those tools may provide NP affected by multiple disabilities with brief periods of positive stimulation. Vocal output communication aids and/or speech-generating devices may enable NP to have social interactions mediated by a caregiver. Computer-mediated programs may ensure requests and choices of desired items with leisure and occupation options [23].
Recently, new technologies have rapidly been developed. VR enables ecological validity, behavioral tracking, experimental control, and immersive environments similar to those of real life. Serious Games promotes positive interactions and active participation. TR as part of telemedicine, offers the assessment, rehabilitation, and supervision of patients remotely, either in a synchronous or in an asynchronous modality. Robot-assisted interventions may be implemented in clinical or medical settings and may be used to rehabilitate patients with stroke. Both assessment and rehabilitative purposes may be favorably achieved [24,25]. Nevertheless, sporadic contributions have been published combining those technological solutions in cognitive rehabilitation (CR) [26,27].
CR is broadly defined as a systematic and oriented cognitive therapy focused on achieving and/or pursuing functional modifications by (a) restoring or fostering previous acquired patterns of behavior or (b) teaching or empowering new patterns of cognitive activity and compensatory mechanisms for impaired neurological systems [28]. Accordingly, CR may include either restorative or compensatory approaches. Restorative strategies are based on exercise principles. The repetitive exercise of neural connectivity supporting cognitive function will enhance the learning process of new skills, which will restore the damage caused by the injury. Restorative approaches rely on neuroplasticity. Thus, intact or nonimpaired neurons and neural circuits would replace the lost functions. Conversely, compensative strategies rely on the substitution of the neural pathways previously necessary to achieve a specific task. By replacing functional mechanisms, modalities are acquired to achieve specific goals. Both approaches are probably combined in a rehabilitative intervention because the cognitive processes stimulated during a compensatory training can easily enhance and consolidate neural connectivity or new learning [29,30].
Considering the above, the aim of the present position paper is to propose a review of the literature about technology-aided rehabilitative programs in NP, emphasizing how technologies can be helpful to overcome neurological insults. Moreover, attention is paid to the effectiveness and the suitability of technology-based interventions for communication and leisure skills. Finally, a new perspective proposal on the combination and matching of AT, TR, and VR in NP is presented.

2. Cognitive Deficits and Technological Supports

Patients with neurological injuries are commonly recognized to be affected by cognitive deficits (e.g., limited executive functions), communication disorders (e.g., lack of speech), motor impairments (e.g., gait or locomotion incapacities), and/or sensorial abnormalities (e.g., hearing or vision loss). Those impairments may negatively impact patients’ daily functioning, causing an increased burden on either caregivers or professionals. Although standard measures such as cognitive scales have been largely adopted in clinical settings, relevant limitations are frequently outlined. Thus, the patient’s assessment requires intensive work, result grading has poor resolution (i.e., mild/moderate/severe/profound), and there is no interactive feedback based on large datasets. Furthermore, for testers’ data to be considered reliable, intensive training would be mandatory. To tackle this issue, one may consider eye tracking, quantifying significant parameters such as amplitude, latency, frequency, and stability which capture objective and reliable dependent variables. Large datasets can be collected with sophisticated methods based on machine learning. Increasing evidence suggests that eye tracking information highly correlates with standard cognitive assessment scales, strongly supporting the idea that eye tracking can be used to evaluate cognitive states, disease severity and progression in NP [31].
AT, VR, and TR may be useful for both assessment and rehabilitative purposes because they provide a strong and valid support to evaluate and rehabilitate the cognitive functioning. For example, AT may be adopted in post-coma patients with disorders of consciousness to evaluate if a diagnosis of vegetative state is reliable or a more favorable diagnosis of minimally conscious state can be made [32]. Additionally, VR may be implemented as a smart aging platform for assessing early stages of cognitive impairments in patients with neurodegenerative diseases [33]. Moreover, TR may be used to positively supervise patients with neurological impairments remotely with a dual objective, namely, (a) evaluation and (b) rehabilitation [34,35].

3. Method and Selective Review

A computerized search was performed in SCOPUS. Neurological Populations, neurological impairments, neurodevelopmental disorders, ADHD, ASD, cerebral palsy, neurodegenerative diseases, acquired brain injuries, spinal cord injury, stroke, assessment, recovery, rehabilitation, AT, VR, TR, multiple sclerosis, amyotrophic lateral sclerosis, Alzheimer’s and Parkinson’s disease, post-coma, vegetative state, and minimally conscious state were merged as keywords. A manual search was additionally included of the published literature. The eligibility criteria were (a) an empirical contribution with a technology-based program, (b) at least a participant with neurological impairments, (c) last five years (i.e., 2018–2022 as the range interval of publication), and (d) English as the language of the paper. Theoretical papers, book chapters, conference papers, and/or proceedings were excluded since their inclusion would exceed the purpose of the current article.
Similarly, we did neither a meta-analysis nor a systematic review and we acknowledged this specific point as a limitation of our work (see the final section). Initially, 80 records were identified. Duplicates were preliminarily removed. Two scholars with a 10 year professional expertise on technology-aided interventions and multiple disabilities independently assessed and screened the selected contributions. First, a title assessment was conducted, and 37 contributions were retained. Second, an abstract evaluation was carried out, with 28 contributions screened. Third, a final decision based on the aforementioned eligibility criteria was made on 15 contributions reviewed. Whenever a disagreement occurred, a third scholar was involved to take a final decision. An interrater agreement of 97% was finally recorded. Initially, overall, the 15 papers reviewed (i.e., 5for each identified group, namely, AT, VR, and TR) [36,37,38,39,40,41,42,43,44,45,46,47,48,49,50] were grouped according to the technology used (i.e., AT, VR, or TR). Among the reviewed studies, five were based on single-subject experimental designs [36,44,45,49,50], four were between-groups experimental comparisons [38,39,40,47], three were randomized controlled trials [37,42,46], one included an experimental training [48], one was a prospective cross-sectional study [43], and finally one was a protocol with a transcranial deep stimulation [41]. The review process is available upon request to the corresponding author. For practical reasons, the contribution of each group is detailed. Table 1 summarizes the reviewed studies arranged in alphabetic order.
Bertomeu-Motos et al. [38] assessed the environment control interface (ECI) developed under a multimodal interface able to analyze and extract relevant information from the environment as well as from the identification of residual skills, behaviors, and intentions of the user (i.e., AIDE). Eight adults aged between 22 and 58 years with different neurological diseases such as muscular atrophy, dystrophy, ischemic stroke, and spinal cord injury participated in a simulated scenario using a two-screen layout: one with the ECI, and the other with a simulated environment, developed for this specific purpose. The sensorimotor rhythm and the horizontal oculo-version were used to measure the online monitoring of the ECI after the user training and system calibration. The participants were requested to perform simulated tasks consisting of daily living actions, such as drinking, switching on a lamp, or raising the bed head, for ten minutes. Two experimental conditions were evaluated, namely, (a) the AIDE mode, using a prediction model useful to recognize the user intention facilitating the scan, and (b) the Manual mode, without a prediction model. The results evidenced that the mean task time spent in the AIDE mode was shorter if compared to that of the manual mode. Thus, the participants positively achieved more tasks in the AIDE mode with statistical differences between conditions. Additionally, all the participants correctly performed 90% of the activities using the AIDE mode. Conversely, at least three steps were necessary in the manual mode.
Leonardi et al. [45] evaluated cognitive outcomes after rehabilitation training mediated by the VR rehabilitation system (VRRS) in patients diagnosed with multiple sclerosis (MS). Thirty participants with relapsing/remitting MS were enrolled. They were aged between 50 and 65 years. All the participants were equally and randomly assigned either to a control group (CG) or to an experimental group (EG). The CG received traditional cognitive rehabilitation training. The EG was exposed to a VR-based intervention. Both groups underwent an identical amount of cognitive rehabilitation, three times per week, for 8 weeks. They were assessed through neuropsychological evaluation before (T0) and after (T1) the rehabilitation program. The data demonstrated that both rehabilitative approaches improved the participants’ mood and visuospatial skills. Nevertheless, only the EG showed a significant improvement in specific cognitive domains such as learning abilities, short-term verbal memory, lexical access ability, and quality of life related to mental states.
Eilam-Stock, George, and Charvet [41] reported a case study of a 29-year-old man with traumatic brain injury (TBI) with persisting negative consequences on both cognitive and emotional domains. A remote transcranial direct current stimulation (tDCS) paired with cognitive training was supplied. Neuropsychological measures were recorded before and after the participant completed a set of 20 daily sessions of remote supervision (RS.tDCS; 2.0 mA × 20 min, left anodal dorsolateral prefrontal cortex montage). During the stimulation period, the participant completed adaptive cognitive training. All the procedures were implemented at home and monitored remotely in real time through videoconferences with a study technician. The results showed a significant enhancement and empowerment on tests of attention and working memory, semantic fluency and information processing speed. The participant’s mood improved as well. The contribution empirically corroborated the effectiveness and the suitability of an RS-tDCS-based program to improve cognitive skills following a TBI.

4. Communication, Internet, and Leisure Opportunities

Stroke, traumatic brain injuries, cerebral palsy, congenital conditions, and/or neurodegenerative diseases may cause negative communication outcomes such as apraxia of speech, aphasia, dysarthria, lack of speech. In fact, individuals with neurological impairments may experience systematic failures in communication skills, with poor social interactions and limited opportunities to positively satisfy their personal needs [51,52,53]. Accordingly, persons with neurological disorders may be unable to easily understand what is being said or told. Additionally, they may be isolated and passive, with negative consequences on their quality of life and increased caregivers’ burden [54,55]. Thus, restricted education and/or employment opportunities are acknowledged. Usually, a traditional speech-mediated intervention with a therapist is recommended [56]. Rehabilitative programs are commonly delivered within a medical setting. That is, limited generalized strategies and learning processes may be recognized [57]. Technology-aided interventions may be considered highly encouraging and promising to overcome this issue [58,59].
For example, one may envisage the use of speech-generating devices to promote requesting behavior. Specific hardware (e.g., tablets, IPAD, or IPOD) with adapted software capable of capturing the participants’ needs may be adopted [60,61]. Both neurodevelopmental disorders and neurodegenerative diseases may be targeted [62,63]. Furthermore, one may use computerized systems with adapted software to support requests and choice behaviors among different options organized in a hierarchical way [59,60]. Furthermore, smartphones and microswitches to support the self-management of telephone calls and short text messages may be used [64,65]. Finally, one might consider internet access through technological aids supporting leisure opportunities [66,67].
VR setups within emerging technologies have recently attracted relevant attention and increasingly become affordable and accessible to be implemented in clinical settings. Additionally, those programs may represent a valid tool for both assessment and recovery of cognitive functioning [68,69]. Communication-based interventions may be fostered. Thus, ecological validity, experimental control, and behavioral tracking as mentioned above may be ensured with real-word immersive and/or non-immersive environments [70,71,72]. Nevertheless, few studies have been conducted, up to date, on the enhancement of communication skills in NP through VR setups [73,74]. Finally, TR has the potential to enable high-frequency treatment remotely. An illustrative example is outlined by Uslu et al. [75] who investigated the effectiveness and the suitability of a new tablet-based telerehabilitation speech and language therapy-based app in patients with aphasia. A randomized, controlled, evaluator-blinded, multicenter trial protocol was formulated. A systematic comparison with a tele-rehabilitative cognitive training was carried out. A sample of 100 patients with aphasia was considered. The patients were assigned to two groups in a 1:1 ratio stratified by trial, site, and severity of impairment. Both groups were trained over a period of 4 weeks for 2 h per day. The experimental group devoted 80% of the training to the new app managed through a tablet and 20% of the time to the cognitive training; the treatment percentages were reversed for the control group. The primary outcome included an understandable verbal communication. The secondary outcome included the intelligibility of the verbal communication, the impairment of both expressive and receptive communication, and confrontation naming. Further outcomes measures were the quality of life and acceptance (i.e., the usability of the system combined with the subjective experience).

5. The New Perspective Proposal

In the light of the above, the possibility to combine and match technology-based setups using AT, TR, and VR devices and tools for enabling the reliable assessment and recovery of cognitive functioning in NP [76,77] could be considered useful. The novelty feature may include a unique combined technology-aided strategy helpful to promote an active role, constructive engagement, and positive participation in NP. Individuals with neurological impairments may be involved in immersive daily situations similar to those of the real life. The mediation of both families and caregivers might be fostered. The remotely supervised assessment and recovery of cognitive functions might be pursued. Customized solutions, tailored options, and individualized devices, equipment, or tools might be envisaged. An integrated and combined technology-mediated program can profitably be built to support NP and their families and caregivers in daily life.
The implementation of this proposal could improve the patient’s quality of life in preventing isolation and passivity and reduce both caregivers and families’ burden [78]. Specifically, it is conceived for matching the three approaches in a unique suitable setup to be implemented in clinical settings [79]. The TR strategy might additionally be used remotely while the patient is living at home [80]. Systematic comparisons between healthy individuals and matched NP may be sought [81]. Basic AT devices may fill the existing gap between an individual’s behavioral repertoire and environmental requests [82]. VR-based programs rely on fully immersive illusions. A first-person centered perspective is usually considered. A customized solution is commonly adopted [83]. TR should ensure remote monitoring and supervision in a synchronous or asynchronous modality [84,85].
Although no specific rules exist, the following steps may be emphasized to maximize and optimize the learning process. First, a suitable behavioral response available in the individual’s repertoire should be identified. Second, a suitable technological setup capable of detecting the behavioral response and suitable for delivering environmental consequences should be implemented. Third, highly motivating, pleasant, and rewarding events to be used as positive reinforcers should be selected through a formal screening [86]. Whenever those guidelines are applied, one may reasonably argue that the learning process will be successfully fostered [87].
The proposed matching between technological solutions may represent a valid alternative to overcome the current COVID-19 pandemic situation, which worldwide requires effective and customized technological options [88,89]. Next to healthcare and well-being during the COVID-19 pandemic era [90,91], a growing interest has recently been devoted to NP. Some illustrative and practical examples are presented. For instance, one may rely on the application of serious games among children and adolescents with neurodevelopmental disorders with both fun and educational purposes [92,93]. Otherwise, patients with acquired brain injuries and post-coma conditions might be assessed and helped recover through technological-aided programs [94]. Additionally, participants with neurodegenerative diseases may be enabled to communicate with distant partners via technology-aided interventions [95]. Accordingly, positive outcomes in an individual’s health and well-being may be enhanced. Caregivers and families’ burden may be significantly reduced [96,97,98]. A full social inclusion of NP in daily settings may be fostered [99,100]. The combination should be highly customized and rigorously tailored on the user and the targeted cognitive impairments investigated. Depending on the participant’s level of functioning, the diagnosis and capacity/possibility of recovery, different combinations might be envisaged. For example, for individuals with multiple disabilities and a low behavioral repertoire, assistive technology-based devices integrated with serious games can be assessed. For patients with neurodegenerative diseases and cognitive decline, VR setups and telerehabilitation can be merged. For patients with post-coma and disorders of consciousness, based on their level of functioning, an assistive technology device combined with a TR service could be considered. Systematic empirical investigations/studies in this regard to evaluate each specific situation are mandatory.

6. Discussion

The literature available on the use of technological-aided interventions in individuals affected by neurological conditions emphasizes the suitability and the effectiveness of such programs for both assessment and recovery purposes [101,102,103]. AT profitably ensures a functional bridge between the behavioral repertoire and environmental requests enabling people to acquire independence and self-determination [104,105]. VR positively presents the real word with immersive and/or non-immersive situations with a fully sensorial pleasant experience [106,107]. TR helps clinicians and practitioners in remote assessment and cognitive daily rehabilitation [108,109].
Unfortunately, no empirical evidence exists on the effects of using combined and matched technologies in a unique program. Matching the technological solutions may provide NP with (a) a further active role, (b) an advantageous and supportive constructive engagement, (c) immersive real-word conditions with fully sensorial experience, and (d) remote assessment and rehabilitation. Executive functions (e.g., sustained attention and working memory) and communication skills (i.e., verbal and written) may be relevantly promoted. The affordability, accessibility, effectiveness, and suitability in daily settings of the combined strategy should be carefully and rigorously investigated through empirical and systematic contributions involving different neurological conditions. The sustainability with regard to (a) financial resources, (b) human resources, and (c) environmental availability should be also evaluated, as well as the targeted behaviors (e.g., communication skills, challenging behaviors, social and/or emotional behaviors, executive functions) [110,111,112,113].
Indeed, leisure, positive participation, functional occupation, sustained attention, and communication may be significantly enhanced. A purposeful behavior with an active role might be additionally documented [114,115]. Negative outcomes in the participants’ mood such as depression and passivity may be meaningfully prevented [116,117]. The helpful use of AT and combined emerging technologies in NP should be empirically corroborated [118,119]. VR setups were useful to promote positive engagement [120,121]. TR provided remote monitoring and recovery [122]. Because the COVID-19 pandemic may be deleterious and may seriously hamper the independence and self-determination, an active role, constructive engagement, and positive participation of NP in daily activities should primarily be considered. To overcome negative consequences in NP quality of life and caregivers’ burden, combined and integrated technology-aided solutions may be viewed as highly recommended [123]. Matching the three strategies through a careful and customized selection of devices and equipment may be highly warranted and may provide NP with a full, valuable, and complete support to tackle daily issues [124].

7. Limitations and Future Directions

Although relevant, encouraging, and promising, our perspective proposal has some limitations. First, it lacks an empirical demonstration. Second, we did not adequately differentiate the neurological conditions (e.g., neurodevelopmental disorders and neurodegenerative diseases). Third, neither a systematic review nor a meta-analysis is present in the available literature. Rather, a selective review was conducted.
Beside supporting our proposal with empirical studies, future directions for both research and clinical practice should deal with the following topics: (a) systematic comparisons between different technological solutions, devices, or tools, (b) generalization, maintenance, and follow-up experimental phases, (c) extension to new technology-based strategies which should be systematically customer-tailored, and (d) preference checks or social validation procedures including external raters (e.g., caregivers, neurologists, physiotherapists, or psychologists) such as professional experts to support the clinical validity [125].

8. Conclusions

Neurological populations (NP) commonly experience impairments such as motor and sensory delays and communication and intellectual disabilities, and the COVID-19 pandemic has worsened their clinical conditions. The compatibility of technology-assisted interventions, such as assistive technology-based interventions, telerehabilitation, and virtual reality, has rarely been investigated. This paper provides a perspective proposal on the pairing of the three previously mentioned technological solutions, outlines a concise background on the use of technology-assisted solutions, argues on the effectiveness and suitability of technology-mediated programs, and presents an integrative proposal to support cognitive rehabilitation.

Author Contributions

Author Contributions. F.S. conceived and drafted the paper. A.L. and K.A. edited and critically revised the manuscript. L.A.V. and M.C. revised the paper. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Barro, C.; Zetterberg, H. The blood biomarkers puzzle—A review of protein biomarkers in neurodegenerative diseases. J. Neurosci. Methods 2021, 361, 109281. [Google Scholar] [CrossRef]
  2. Pape, T.L.; High, W.M.; St. Andre, J.; Evans, C.; Smith, B.; Shandera-Ochsner, A.L.; Wingo, J.; Moallem, I.; Baldassarre, M.; Babcock-Parziale, J. Diagnostic accuracy studies in mild traumatic brain injury: A systematic review and descriptive analysis of published evidence. PM R 2013, 5, 856–881. [Google Scholar] [CrossRef]
  3. Orrù, G.; Bertelloni, D.; Diolaiuti, F.; Mucci, F.; Di Giuseppe, M.; Biella, M.; Gemignani, A.; Ciacchini, R.; Conversano, C. Long-COVID syndrome? A study on the persistence of neurological, psychological and physiological symptoms. Healthcare 2021, 9, 575. [Google Scholar] [CrossRef] [PubMed]
  4. Abrams, J.Y.; Oster, M.E.; Godfred-Cato, S.E.; Bryant, B.; Datta, S.D.; Campbell, A.P.; Leung, J.W.; Tsang, C.A.; Pierce, T.J.; Kennedy, J.L.; et al. Factors linked to severe outcomes in multisystem inflammatory syndrome in children (MIS-C) in the USA: A retrospective surveillance study. Lancet Child Adolesc. Health 2021, 5, 323–331. [Google Scholar] [CrossRef] [PubMed]
  5. Lenka, A.; Louis, E.D. Do we belittle essential tremor by calling it a syndrome rather than a disease? Yes. Front. Neurol. 2020, 11, 522687. [Google Scholar] [CrossRef] [PubMed]
  6. Bogetz, J.F.; Trowbridge, A.; Lewis, H.; Shipman, K.J.; Jonas, D.; Hauer, J.; Rosenberg, A.R. Parents are the experts: A qualitative study of the experiences of parents of children with severe neurological impairment during decision-making. J. Pain Symptom Manag. 2021, 62, 1117–1125. [Google Scholar] [CrossRef]
  7. Solomon, D.L.; Dirlikov, B.; Shem, K.L.; Elliott, C.S. The time burden of specialty clinic visits in persons with neurologic disease: A case for universal telemedicine coverage. Front. Neurol. 2021, 12, 559024. [Google Scholar] [CrossRef]
  8. Walzl, D.; Solomon, A.J.; Stone, J. Functional neurological disorder and multiple sclerosis: A systematic review of misdiagnosis and clinical overlap. J. Neurol. 2022, 269, 654–663. [Google Scholar] [CrossRef]
  9. Crook, H.; Raza, S.; Nowell, J.; Young, M.; Edison, P. Long COVID—Mechanisms, risk factors, and management. BMJ 2021, 374, n1648. [Google Scholar] [CrossRef]
  10. Katz, D.I.; Bernick, C.; Dodick, D.W.; Mez, J.; Mariani, M.L.; Adler, C.H.; Alosco, M.L.; Balcer, L.J.; Banks, S.J.; Barr, W.B.; et al. National institute of neurological disorders and stroke consensus diagnostic criteria for traumatic encephalopathy syndrome. Neurology 2021, 96, 848–863. [Google Scholar] [CrossRef]
  11. Parrinello, N.; Napieralski, J.; Gerlach, A.L.; Pohl, A. Embodied feelings–A meta-analysis on the relation of emotion intensity perception and interoceptive accuracy. Physiol. Behav. 2022, 254, 113904. [Google Scholar] [CrossRef]
  12. Webster, P.J.; Wang, S.; Li, X. Review: Posed vs. genuine facial emotion recognition and expression in autism and implications for intervention. Front. Psychol. 2021, 12, 653112. [Google Scholar] [CrossRef] [PubMed]
  13. Buttermore, E.; Chamberlain, S.; Cody, J.; Costain, G.; Dang, L.; DeWoody, A.; DeWoody, Y.; Dies, K.; Eichler, E.; Girirajan, S.; et al. Neurodevelopmental copy-number variants: A roadmap to improving outcomes by uniting patient advocates, researchers, and clinicians for collective impact. Am. J. Hum. Genet. 2022, 109, 1353–1365. [Google Scholar] [CrossRef] [PubMed]
  14. Wu, K.-C.; Su, Y.; Chu, F.; Chen, A.T.; Zaslavsky, O. Behavioral change factors and retention in web-based interventions for informal caregivers of people living with dementia: Scoping review. J. Med. Internet Res. 2022, 24, e38595. [Google Scholar] [CrossRef]
  15. Iodice, F.; Cassano, V.; Rossini, P.M. Direct and indirect neurological, cognitive, and behavioral effects of COVID-19 on the healthy elderly, mild-cognitive-impairment, and Alzheimer’s disease populations. Neurol. Sci. 2021, 42, 455–465. [Google Scholar] [CrossRef]
  16. Vinciguerra, C.; Federico, A. Neurological music therapy during the COVID-19 outbreak: Updates and future challenges. Neurol. Sci. 2022, 43, 3473–3478. [Google Scholar] [CrossRef]
  17. Nami, M.; Thatcher, R.; Kashou, N.; Lopes, D.; Lobo, M.; Bolanos, J.F.; Morris, K.; Sadri, M.; Bustos, T.; Sanchez, G.E.; et al. A proposed brain-, spine-, and mental-health screening methodology (NEUROSCREEN) for healthcare systems: Position of the society for brain mapping and therapeutics. J. Alzheimer’s Dis. 2022, 86, 21–42. [Google Scholar] [CrossRef]
  18. Gonzalez, A.; Garcia, L.; Kilby, J.; McNair, P. Robotic devices for pediatric rehabilitation: A review of design features. Biomed. Eng. Online 2021, 20, 1–33. [Google Scholar] [CrossRef] [PubMed]
  19. Truijen, S.; Abdullahi, A.; Bijsterbosch, D.; van Zoest, E.; Conijn, M.; Wang, Y.; Struyf, N.; Saeys, W. Effect of home-based virtual reality training and telerehabilitation on balance in individuals with Parkinson disease, multiple sclerosis, and stroke: A systematic review and meta-analysis. Neurol. Sci. 2022, 43, 2995–3006. [Google Scholar] [CrossRef] [PubMed]
  20. Özden, F.; Özkeskin, M.; Ak, S.M. Physical exercise intervention via telerehabilitation in patients with neurological disorders: A narrative literature review. Egypt J. Neurol. Psychiatr. Neurosurg. 2022, 58, 1–9. [Google Scholar] [CrossRef]
  21. Stasolla, F.; Caffò, A.O.; Ciarmoli, D.; Albano, V. Promoting object manipulation and reducing tongue protrusion in seven children with angelman syndrome and developmental disabilities through microswitch-cluster technology: A research extension. J. Dev. Phys. Disabil. 2021, 33, 799–817. [Google Scholar] [CrossRef]
  22. Ciarmoli, D.; Stasolla, F. The Use of Alternative Augmentative Communication in Children and Adolescents With Neurodevelopmental Disorders: A Critical Review. Curr. Develop. Disord. Rep. 2023, 10, 14–19. [Google Scholar] [CrossRef]
  23. Lancioni, G.E.; O’Reilly, M.F.; Sigafoos, J.; D’Amico, F.; Spica, A.; Buonocunto, F.; Del Gaudio, V.; Lanzilotti, C.; Navarro, J. Mainstream technology as basic support for individuals with extensive neuro-motor impairments and absence of verbal skills. Adv. Neurodev. Disord. 2021, 5, 85–92. [Google Scholar] [CrossRef]
  24. Gopal, A.; Hsu, W.-Y.; Allen, D.D.; Bove, R. Remote assessments of hand function in neurological disorders: Systematic review. JMIR Rehabil. Assist. Technol. 2022, 9, e33157. [Google Scholar] [CrossRef] [PubMed]
  25. Pituch, E.; Bindiu, A.M.; Grondin, M.; Bottari, C. Parenting with a physical disability and cognitive impairments: A scoping review of the needs expressed by parents. Disabil. Rehabil. 2022, 44, 3285–3300. [Google Scholar] [CrossRef] [PubMed]
  26. Guo, J.; Lo, W.L.A.; Hu, H.; Yan, L.; Li, L. Transcranial ultrasound stimulation applied in ischemic stroke rehabilitation: A review. Front. Neurosci. 2022, 16, 1195. [Google Scholar] [CrossRef] [PubMed]
  27. Waerling, R.D.; Kjaer, T.W. A systematic review of impairment focussed technology in neurology. Disabil. Rehabil. Assist. Technol. 2022, 17, 234–247. [Google Scholar] [CrossRef]
  28. Park, J.-H.; Ah Lee, S. The fragility of temporal memory in Alzheimer’s disease. J. Alzheimer’s Dis. 2021, 79, 1631–1646. [Google Scholar] [CrossRef]
  29. Yamamoto, S.; Levin, H.S.; Prough, D.S. Mild, moderate and severe: Terminology implications for clinical and experimental traumatic brain injury. Curr. Opin. Neurol. 2018, 31, 672–680. [Google Scholar] [CrossRef]
  30. Rocca, M.A.; Schoonheim, M.M.; Valsasina, P.; Geurts, J.J.G.; Filippi, M. Task- and resting-state fMRI studies in multiple sclerosis: From regions to systems and time-varying analysis. Current status and future perspective. NeuroImage Clin. 2022, 35, 103076. [Google Scholar] [CrossRef]
  31. Tao, L.; Wang, Q.; Liu, D.; Wang, J.; Zhu, Z.; Feng, L. Eye tracking metrics to screen and assess cognitive impairment in patients with neurological disorders. Neurol. Sci. 2020, 41, 1697–1704. [Google Scholar] [CrossRef]
  32. Stasolla, F.; Perilli, V.; Boccasini, A.; Caffò, A.O.; Damiani, R. Intervention options for assessing and recovering post-coma persons in a vegetative state. In Advances in Psychology Research; Columbus, A.M., Ed.; Nova Publishers: New York, NY, USA, 2017; pp. 179–199. [Google Scholar]
  33. Bottiroli, S.; Bernini, S.; Cavallini, E.; Sinforiani, E.; Zucchella, C.; Pazzi, S.; Cristiani, P.; Vecchi, T.; Tost, D.; Sandrini, G.; et al. The smart aging platform for assessing early phases of cognitive impairment in patients with neurodegenerative diseases. Front. Psychol. 2021, 12, 1280. [Google Scholar] [CrossRef] [PubMed]
  34. Martins Gaspar, A.G.; Lapão, L.V. eHealth for addressing balance disorders in the elderly: Systematic review. J. Med. Internet Res. 2021, 23, e22215. [Google Scholar] [CrossRef]
  35. Matamala-Gomez, M.; Maisto, M.; Montana, J.I.; Mavrodiev, P.A.; Baglio, F.; Rossetto, F.; Mantovani, F.; Riva, G.; Realdon, O. The role of engagement in teleneurorehabilitation: A systematic review. Front. Neurol. 2020, 11, 354. [Google Scholar] [CrossRef] [PubMed]
  36. Arroyo-Ferrer, A.; de Noreña, D.; Serrano, J.I.; Ríos-Lago, M.; Romero, J.P. Cognitive rehabilitation in a case of traumatic brain injury using EEG-based neurofeedback in comparison to conventional methods. J. Integr. Neurosci. 2021, 20, 449–457. [Google Scholar] [CrossRef]
  37. Bekkers, E.M.J.; Mirelman, A.; Alcock, L.; Rochester, L.; Nieuwhof, F.; Bloem, B.R.; Pelosin, E.; Avanzino, L.; Cereatti, A.; Della Croce, U.; et al. Do patients with Parkinson’s disease with freezing of gait respond differently than those without to treadmill training augmented by virtual reality? Neurorehabil. Neural Repair 2020, 34, 440–449. [Google Scholar] [CrossRef] [PubMed]
  38. Bertomeu-Motos, A.; Ezquerro, S.; Barios, J.A.; Lledó, L.D.; Domingo, S.; Nann, M.; Martin, S.; Soekadar, S.R.; Garcia-Aracil, N. User activity recognition system to improve the performance of environmental control interfaces: A pilot study with patients. J. Neuroeng. Rehabil. 2019, 16, 1–9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  39. Calabrò, R.S.; Naro, A.; Cimino, V.; Buda, A.; Paladina, G.; Di Lorenzo, G.; Manuli, A.; Milardi, D.; Bramanti, P.; Bramanti, A. Improving motor performance in Parkinson’s disease: A preliminary study on the promising use of the computer assisted virtual reality environment (CAREN). Neurol. Sci. 2020, 41, 933–941. [Google Scholar] [CrossRef] [PubMed]
  40. Capodieci, A.; Romano, M.; Castro, E.; Di Lieto, M.C.; Bonetti, S.; Spoglianti, S.; Pecini, C. Executive functions and rapid automatized naming: A new tele-rehabilitation approach in children with language and learning disorders. Child 2022, 9, 822. [Google Scholar] [CrossRef]
  41. Eilam-Stock, T.; George, A.; Charvet, L.E. Cognitive telerehabilitation with transcranial direct current stimulation improves cognitive and emotional functioning following a traumatic brain injury: A case study. Arch. Clin. Neuropsychol. 2021, 36, 442–453. [Google Scholar] [CrossRef]
  42. Gerber, S.M.; Schütz, N.; Uslu, A.S.; Schmidt, N.; Röthlisberger, C.; Wyss, P.; Perny, S.; Wyss, C.; Koenig-Bruhin, M.; Urwyler, P.; et al. Therapist-guided tablet-based telerehabilitation for patients with aphasia: Proof-of-concept and usability study. JMIR Rehabil. Assist. Technol. 2019, 6, e13163. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Jamali, A.R.; Alizadeh Zarei, M.; Sanjari, M.A.; AkbarFahimi, M.; Saneii, S.H. Randomized controlled trial of occupation performance coaching for families of children with autism spectrum disorder by means of telerehabilitation. Br. J. Occup. Ther. 2022, 85, 308–315. [Google Scholar] [CrossRef]
  44. Jordan, M.; Nogueira, G.N.N.; Brito, A., Jr.; Nohama, P. Virtual keyboard with the prediction of words for children with cerebral palsy. Comput. Methods Programs Biomed. 2020, 192, 105402. [Google Scholar] [CrossRef]
  45. Lancioni, G.E.; Singh, N.N.; O’Reilly, M.F.; Sigafoos, J.; Alberti, G.; Chiariello, V.; Buono, S. Extended smartphone-aided program to sustain daily activities, communication and leisure in individuals with intellectual and sensory-motor disabilities. Res. Dev. Disabil. 2020, 105, 103722. [Google Scholar] [CrossRef]
  46. Leonardi, S.; Maggio, M.G.; Russo, M.; Bramanti, A.; Arcadi, F.A.; Naro, A.; Calabrò, R.S.; De Luca, R. Cognitive recovery in people with relapsing/remitting multiple sclerosis: A randomized clinical trial on virtual reality-based neurorehabilitation. Clin. Neurol. Neurosurg. 2021, 208, 106828. [Google Scholar] [CrossRef]
  47. Maier, M.; Ballester, B.R.; Leiva Banuelos, N.; Duarte Oller, E.; Verschure, P.F.M.J. Adaptive conjunctive cognitive training (ACCT) in virtualreality for chronic stroke patients: A randomized controlled pilot trial. J. Neuroeng. Rehabil. 2020, 17, 42. [Google Scholar] [CrossRef] [Green Version]
  48. Pinter, D.; Kober, S.E.; Fruhwirth, V.; Berger, L.; Damulina, A.; Khalil, M.; Neuper, C.; Wood, G.; Enzinger, C. MRI correlates of cognitive improvement after home-based EEG neurofeedback training in patients with multiple sclerosis: A pilot study. J. Neurol. 2021, 268, 3808–3816. [Google Scholar] [CrossRef]
  49. Stasolla, F.; Caffò, A.O.; Perilli, V.; Boccasini, A.; Damiani, R.; D’Amico, F. Assistive technology for promoting adaptive skills of children with cerebral palsy: Ten cases evaluation. Disabil. Rehabil. Assist. Technol. 2019, 14, 489–502. [Google Scholar] [CrossRef] [PubMed]
  50. Stasolla, F.; Caffò, A.O.; Bottiroli, S.; Ciarmoli, D. An assistive technology program for enabling five adolescents emerging from a minimally conscious state to engage in communication, occupation, and leisure opportunities. Dev. Neurorehabilit. 2022, 25, 193–204. [Google Scholar] [CrossRef]
  51. Cole, S.; Deci, E.L.; Zhao, H.; Wang, W. How environmental and psychological factors interact with travel participation after spinal cord injury. Rehabil. Psychol. 2022, 67, 152–161. [Google Scholar] [CrossRef]
  52. Vaughan-Graham, J.; Brooks, D.; Rose, L.; Nejat, G.; Pons, J.; Patterson, K. Exoskeleton use in post-stroke gait rehabilitation: A qualitative study of the perspectives of persons post-stroke and physiotherapists. J. Neuroeng. Rehabil. 2020, 17, 1–15. [Google Scholar] [CrossRef]
  53. Mickens, M.N.; Perrin, P.B.; Aguayo, A.; Rabago, B.; Macías-Islas, M.A.; Arango-Lasprilla, J.C. Mediational model of multiple sclerosis impairments, family needs, and caregiver mental health in Guadalajara, Mexico. Behav. Neurol. 2018, 2018, 1–11. [Google Scholar] [CrossRef]
  54. Sarhan, A.A.; El-Sharkawy, K.A.; Mahmoudy, A.M.; Hashim, N.A. Burden of multiple sclerosis: Impact on the patient, family and society: Burden of multiple sclerosis. Mult. Scler. Relat. Disord. 2022, 63, 103864. [Google Scholar] [CrossRef]
  55. Ngadimon, I.W.; Aledo-Serrano, A.; Arulsamy, A.; Mohan, D.; Khoo, C.S.; Cheong, W.L.; Shaikh, M.F. An interplay between post-traumatic epilepsy and associated cognitive decline: A systematic review. Front. Neurol. 2022, 13, 248. [Google Scholar] [CrossRef]
  56. Schwab, S.M.; Dugan, S.; Riley, M.A. Reciprocal influence of mobility and speech-language: Advancing physical therapy and speech therapy cotreatment and collaboration for adults with neurological conditions. Phys. Ther. 2021, 101, pzab196. [Google Scholar] [CrossRef]
  57. Willms, S.; Abel, M.; Karni, A.; Gal, C.; Doyon, J.; King, B.R.; Classen, J.; Rumpf, J.-J.; Buccino, G.; Pellicano, A.; et al. Motor sequence learning in patients with ideomotor apraxia: Effects of long-term training. Neuropsychologia 2021, 159, 107921. [Google Scholar] [CrossRef]
  58. D’Amico, F.; Lancioni, G.E.; Buonocunto, F.; Ricci, C.; Fiore, P. Technology-aided leisure and communication support in extensive neuro-motor and communication impairments. Eur. J. Phys. Rehabil. Med. 2019, 55, 682–686. [Google Scholar] [CrossRef]
  59. D’Amico, F.; Boscia, F.; Cannone, A.; Alberti, G.; Lancioni, G. Everyday technology to support basic communication and leisure of individuals with extensive neuro-motor and speech impairments. J. Enabling Technol. 2022, 16, 243–249. [Google Scholar] [CrossRef]
  60. Pittman, C.A.; Ward, B.K.; Nieman, C.L. A review of adult-onset hearing loss: A primer for neurologists. Curr. Treat. Options Neurol. 2021, 23, 1–17. [Google Scholar] [CrossRef] [PubMed]
  61. Zampogna, A.; Mileti, I.; Palermo, E.; Celletti, C.; Paoloni, M.; Manoni, A.; Mazzetta, I.; Costa, G.D.; Pérez-López, C.; Camerota, F.; et al. Fifteen years of wireless sensors for balance assessment in neurological disorders. Sensors 2020, 20, 3247. [Google Scholar] [CrossRef] [PubMed]
  62. Ouellette, J.; Lacoste, B. From neurodevelopmental to neurodegenerative disorders: The vascular continuum. Front. AgingNeurosci. 2021, 13, 647. [Google Scholar] [CrossRef] [PubMed]
  63. Yang, Y.; Zhou, X.; Liu, X.; Song, R.; Gao, Y.; Wang, S. Implications of FBXW7 in neurodevelopment and neurodegeneration: Molecular mechanisms and therapeutic potential. Front. Cell Neurosci. 2021, 15, 736008. [Google Scholar] [CrossRef] [PubMed]
  64. Stasolla, F.; Damiani, R.; Perilli, V.; D’Amico, F.; Caffò, A.O.; Stella, A.; Albano, V.; Damato, C.; Di Leone, A. Computer and microswitch-based programs to improve academic activities by six children with cerebral palsy. Res. Dev. Disabil. 2015, 45-46, 1–13. [Google Scholar] [CrossRef] [PubMed]
  65. Stasolla, F.; Caffò, A.O.; Damiani, R.; Perilli, V.; Di Leone, A.; Albano, V. Assistive technology-based programs to promote communication and leisure activities by three children emerged from a minimal conscious state. Cogn. Process. 2015, 16, 69–78. [Google Scholar] [CrossRef]
  66. Stasolla, F.; De Pace, C. Assistive technology to promote leisure and constructive engagement by two boys emerged from a minimal conscious state. Neurorehabilitation 2014, 35, 253–259. [Google Scholar] [CrossRef]
  67. Lancioni, G.E.; Singh, N.N.; O’Reilly, M.F.; Sigafoos, J.; Alberti, G.; Perilli, V.; Grillo, G.; Turi, C. Case series of technology-aided interventions to support leisure and communication in extensive disabilities. Int. J.Dev. Disabil. 2020, 66, 180–189. [Google Scholar] [CrossRef]
  68. Calafiore, D.; Invernizzi, M.; Ammendolia, A.; Marotta, N.; Fortunato, F.; Paolucci, T.; Ferraro, F.; Curci, C.; Cwirlej-Sozanska, A.; de Sire, A. Efficacy of virtual reality and exergaming in improving balance in patients with multiple sclerosis: A systematic review and meta-analysis. Front. Neurol. 2021, 12, 773459. [Google Scholar] [CrossRef]
  69. de Miguel-Rubio, A.; Dolores Rubio, M.; Alba-Rueda, A.; Salazar, A.; Moral-Munoz, J.A.; Lucena-Anton, D. Virtual reality systems for upper limb motor function recovery in patients with spinal cord injury: Systematic review and meta-analysis. JMIR mHealth uHealth 2020, 8, e22537. [Google Scholar] [CrossRef]
  70. Matamala-Gomez, M.; Bottiroli, S.; Realdon, O.; Riva, G.; Galvagni, L.; Platz, T.; Sandrini, G.; De Icco, R.; Tassorelli, C. Telemedicine and virtual reality at time of COVID-19 pandemic: An overview for future perspectives in neurorehabilitation. Front. Neurol. 2021, 12, 646902. [Google Scholar] [CrossRef]
  71. Stasolla, F.; Matamala-Gomez, M.; Bernini, S.; Caffò, A.O.; Bottiroli, S. Virtual reality as a technological-aided solution to support communication in persons with neurodegenerative diseases and acquired brain injury during COVID-19 pandemic. Front. Public Health 2021, 8, 635426. [Google Scholar] [CrossRef]
  72. Stasolla, F.; Bottiroli, S. Fostering daily life skills in young and older adults with neurodegenerative diseases through technological supports. Int. J. Ambient. Comput. Intell. 2020, 11, 1–15. [Google Scholar] [CrossRef]
  73. Lazarou, I.; Nikolopoulos, S.; Petrantonakis, P.C.; Kompatsiaris, I.; Tsolaki, M. EEG-based brain–computer interfaces for communication and rehabilitation of people with motor impairment: A novel approach of the 21st century. Front. Human Neurosci. 2018, 12, 14. [Google Scholar] [CrossRef] [Green Version]
  74. Marin-Pardo, O.; Laine, C.M.; Rennie, M.; Ito, K.L.; Finley, J.; Liew, S.-L. A virtual reality muscle–computer interface for neurorehabilitation in chronic stroke: A pilot study. Sensors 2020, 20, 3754. [Google Scholar] [CrossRef] [PubMed]
  75. Uslu, A.S.; Gerber, S.M.; Schmidt, N.; Röthlisberger, C.; Wyss, P.; Vanbellingen, T.; Schaller, S.; Wyss, C.; Koenig-Bruhin, M.; Berger, T.; et al. Investigating a new tablet-based telerehabilitation app in patients with aphasia: A randomised, controlled, evaluator-blinded, multicentre trial protocol. BMJ Open 2020, 10, e037702. [Google Scholar] [CrossRef] [PubMed]
  76. McKeon, G.L.; Robinson, G.A.; Ryan, A.E.; Blum, S.; Gillis, D.; Finke, C.; Scott, J.G. Cognitive outcomes following anti-N-methyl-D-aspartate receptor encephalitis: A systematic review. J. Clin. Exp. Neuropsychol. 2018, 40, 234–252. [Google Scholar] [CrossRef]
  77. Cacciatore, M.; Raggi, A.; Pilotto, A.; Cristillo, V.; Guastafierro, E.; Toppo, C.; Magnani, F.G.; Sattin, D.; Mariniello, A.; Silvaggi, F.; et al. Neurological and mental health symptoms associated with post-COVID-19 disability in a sample of patients discharged from a COVID-19 ward: A secondary analysis. Int. J. Environ. Res. Public Health 2022, 19, 4242. [Google Scholar] [CrossRef]
  78. Kratz, A.L.; Boileau, N.R.; Sander, A.M.; Nakase-Richardson, R.; Hanks, R.A.; Massengale, J.P.; Miner, J.A.; Carlozzi, N.E. Do emotional distress and functional problems in persons with traumatic brain injury contribute to perceived sleep-related impairment in caregivers? Rehabil. Psychol. 2020, 65, 432–442. [Google Scholar] [CrossRef]
  79. Chen, L.; Zhao, N.; Xu, S. Research progress of imaging technologies for ischemic cerebrovascular diseases. J. Int. Med. Res. 2021, 49, 0300060520972601. [Google Scholar] [CrossRef]
  80. Bernini, S.; Panzarasa, S.; Sinforiani, E.; Quaglini, S.; Cappa, S.F.; Cerami, C.; Tassorelli, C.; Vecchi, T.; Bottiroli, S. HomeCoRe for Telerehabilitation in Mild or Major Neurocognitive Disorders: A Study Protocol for a Randomized Controlled Trial. Front. Neurol. 2021, 12, 752830. [Google Scholar] [CrossRef]
  81. Baur, K.; Schättin, A.; De Bruin, E.D.; Riener, R.; Duarte, J.E.; Wolf, P. Trends in robot-assisted and virtual reality-assisted neuromuscular therapy: A systematic review of health-related multiplayer games. J. Neuroeng. Rehabil. 2018, 15, 107. [Google Scholar] [CrossRef] [Green Version]
  82. Lancioni, G.E.; Singh, N.N.; O’Reilly, M.F.; Sigafoos, J.; D’Amico, F.; Buonocunto, F.; Lanzilotti, C.; Alberti, G.; Navarro, J. Mainstream technology to support basic communication and leisure in people with neurological disorders, motor impairment and lack of speech. Brain Inj. 2020, 34, 921–927. [Google Scholar] [CrossRef]
  83. Wendt, O.; Miller, B. Systematic review documents emerging empirical support for certain applications of iPods® and iPads® in intervention programs for individuals with developmental disabilities, but these are not a “one-size-fits-all” solution. Evid.-Based Commun. Assess. Interv. 2013, 7, 91–96. [Google Scholar] [CrossRef]
  84. Caprì, T.; Nucita, A.; Iannizzotto, G.; Stasolla, F.; Romano, A.; Semino, M.; Giannatiempo, S.; Canegallo, V.; Fabio, R.A. Telerehabilitation for improving adaptive skills of children and young adults with multiple disabilities: A systematic review. Rev. J. Autism Dev. Disord. 2021, 8, 244–252. [Google Scholar] [CrossRef]
  85. de Brito, S.A.F.; Scianni, A.A.; da Cruz Peniche, P.; de Morais Faria, C.D.C. Measurement properties of outcome measures used in neurological telerehabilitation: A systematic review protocol. PLoS ONE 2022, 17, e0265841. [Google Scholar] [CrossRef]
  86. Crawford, M.R.; Schuster, J.W. Using microswitches to teach toy use. J. Dev. Phys. Disabil. 1993, 5, 349–368. [Google Scholar] [CrossRef]
  87. Lancioni, G.E.; Singh, N.N.; O’Reilly, M.; Sigafoos, J.; Alberti, G.; Desideri, L. Programs using stimulation-regulating technologies to promote physical activity in people with intellectual and multiple disabilities: Scoping review. JMIR Rehabil. Assist. Technol. 2022, 9, e35217. [Google Scholar] [CrossRef]
  88. Bernini, S.; Stasolla, F.; Panzarasa, S.; Quaglini, S.; Sinforiani, E.; Sandrini, G.; Vecchi, T.; Tassorelli, C.; Bottiroli, S. Cognitive telerehabilitation for older adults with neurodegenerative diseases in the COVID-19 era: A perspective study. Front. Neurol. 2021, 11, 623933. [Google Scholar] [CrossRef]
  89. Stasolla, F.; Vinci, L.A.; Cusano, M. The integration of assistive technology and virtual reality for assessment and recovery of post-coma patients with disorders of consciousness: A new hypothesis. Front. Psychol. 2022, 13, 905811. [Google Scholar] [CrossRef]
  90. Javed, A. Neurological associations of SARS-CoV-2 infection: A systematic review. CNS Neurol. Disord. Drug Targets 2022, 21, 246–258. [Google Scholar] [CrossRef]
  91. Paredes, I.; Leon, A.M.C.; Lagares, A.; Roldan, L.J.; Perez-Nuñez, A.; González-Leon, P.; Delgado-Fernandez, J.; Eiriz, C.; García-Pérez, D.; Moreno-Gomez, L.M.; et al. Impact of the first wave of the SARS-CoV-2 pandemic on the outcome of neurosurgical patients: A nationwide study in Spain. BMJ Open 2021, 11, e053983. [Google Scholar] [CrossRef]
  92. Jiang, H.; Natarajan, R.; Shuy, Y.K.; Rong, L.; Zhang, M.W.; Vallabhajosyula, R. The use of mobile games in the management of patients with attention deficit hyperactive disorder: A scoping review. Front. Psychiatry 2022, 13, 100. [Google Scholar] [CrossRef] [PubMed]
  93. Kokol, P.; Vošner, H.B.; Završnik, J.; Vermeulen, J.; Shohieb, S.; Peinemann, F. Serious game-based intervention for children with developmental disabilities. Curr. Pediatr. Rev. 2020, 16, 26–32. [Google Scholar] [CrossRef]
  94. Lancioni, G.E.; Bosco, A.; Belardinelli, M.O.; Singh, N.N.; O’Reilly, M.F.; Sigafoos, J. An overview of intervention options for promoting adaptive behavior of persons with acquired brain injury and minimally conscious state. Res. Dev. Disabil. 2010, 31, 1121–1134. [Google Scholar] [CrossRef] [PubMed]
  95. Lancioni, G.E.; Singh, N.N.; O’Reilly, M.F.; Sigafoos, J.; Alberti, G.; Perilli, V.; Chiariello, V.; Grillo, G.; Turi, C. A tablet-based program to enable people with intellectual and other disabilities to access leisure activities and video calls. Disabil. Rehabil. Assist. Technol. 2020, 15, 14–20. [Google Scholar] [CrossRef]
  96. Gordon, M.N.; Heneka, M.T.; Le Page, L.M.; Limberger, C.; Morgan, D.; Tenner, A.J.; Terrando, N.; Willette, A.A.; Willette, S.A. Impact of COVID-19 on the onset and progression of Alzheimer’s Disease and related dementias: A roadmap for future research. Alzheimer’s Dement. 2022, 18, 1038–1046. [Google Scholar] [CrossRef]
  97. Fang, B.; Liu, H.; Yan, E. Association between caregiver depression and elder mistreatment-examining the moderating effect of care recipient neuropsychiatric symptoms and caregiver-perceived burden. J. Gerontol. Ser. B Psychol. Sci. Soc. Sci. 2021, 76, 2098–2111. [Google Scholar] [CrossRef] [PubMed]
  98. Rousseau, M.-C.; Baumstarck, K.; Valkov, M.; Felce, A.; Brisse, C.; Khaldi-Cherif, S.; Loundou, A.; Auquier, P.; de Villemeur, T.B. Impact of severe polyhandicap cared for at home on French informal caregivers’ burden: A cross-sectional study. BMJ Open 2020, 10, e032257. [Google Scholar] [CrossRef] [Green Version]
  99. Bishnoi, A.; Lee, R.; Hu, Y.; Mahoney, J.R.; Hernandez, M.E. Effect of treadmill training interventions on spatiotemporal gait parameters in older adults with neurological disorders: Systematic review and meta-analysis of randomized controlled trials. Int. J. Environ. Res. Public Health 2022, 19, 2824. [Google Scholar] [CrossRef]
  100. Cacciante, L.; Pietà, C.; Rutkowski, S.; Cieślik, B.; Szczepańska-Gieracha, J.; Agostini, M.; Kiper, P. Cognitive telerehabilitation in neurological patients: Systematic review and meta-analysis. Neurol. Sci. 2022, 43, 847–862. [Google Scholar] [CrossRef]
  101. Chock, V.Y.; Bhombal, S.; Variane, G.F.T.; Van Meurs, K.P.; Benitz, W.E. Ductus arteriosus and the preterm brain. Arch. Dis. Child. Fetal. Neonatal. Ed. 2022, 108, 96–101. [Google Scholar] [CrossRef]
  102. Voinescu, A.; Sui, J.; Stanton Fraser, D. Virtual reality in neurorehabilitation: An umbrella review of meta-analyses. J. Clin. Med. 2021, 10, 1478. [Google Scholar] [CrossRef] [PubMed]
  103. Levin, M.F.; Demers, M. Motor learning in neurological rehabilitation. Disabil. Rehabil. 2021, 43, 3445–3453. [Google Scholar] [CrossRef]
  104. Bonilauri, A.; Intra, F.S.; Pugnetti, L.; Baselli, G.; Baglio, F. A systematic review of cerebral functional near-infrared spectroscopy in chronic neurological diseases—Actual applications and future perspectives. Diagnostics 2020, 10, 581. [Google Scholar] [CrossRef] [PubMed]
  105. Lancioni, G.E.; Singh, N.N.; O’Reilly, M.F.; Sigafoos, J.; Green, V.; Chiapparino, C.; Stasolla, F.; Oliva, D. A voice-detecting sensor and a scanning keyboard emulator to support word writing by two boys with extensive motor disabilities. Res. Dev. Disabil. 2009, 30, 203–209. [Google Scholar] [CrossRef]
  106. Lancioni, G.E.; Singh, N.N.; O’Reilly, M.F.; Sigafoos, J.; Chiapparino, C.; Stasolla, F.; Oliva, D. Using an optic sensor and a scanning keyboard emulator to facilitate writing by persons with pervasive motor disabilities. J. Dev. Phys. Disabil. 2007, 19, 593–603. [Google Scholar] [CrossRef]
  107. Stasolla, F.; Perilli, V.; Damiani, R.; Caffò, A.O.; Di Leone, A.; Albano, V.; Stella, A.; Damato, C. A microswitch-cluster program to enhance object manipulation and to reduce hand mouthing by three boys with autism spectrum disorders and intellectual disabilities. Res. Autism Spectr. Disord. 2014, 8, 1071–1078. [Google Scholar] [CrossRef]
  108. Mantovani, E.; Zucchella, C.; Bottiroli, S.; Federico, A.; Giugno, R.; Sandrini, G.; Chiamulera, C.; Tamburin, S. Telemedicine and virtual reality for cognitive rehabilitation: A roadmap for the COVID-19 pandemic. Front. Neurol. 2020, 11, 926. [Google Scholar] [CrossRef]
  109. Bernini, S.; Gerbasi, A.; Panzarasa, S.; Quaglini, S.; Ramusino, M.C.; Costa, A.; Avenali, M.; Tassorelli, C.; Vecchi, T.; Bottiroli, S. Outcomes of a computer-based cognitive training (CoRe) in early phases of cognitive decline: A data-driven cluster analysis. Sci. Rep. 2023, 13, 2175. [Google Scholar] [CrossRef] [PubMed]
  110. Hom, C.L.; Walsh, D.; Fernandez, G.; Tournay, A.; Touchette, P.; Lott, I.T. Cognitive assessment using the rapid assessment for developmental disabilities, second edition (RADD-2). J. Intellect. Disabil. Res. 2021, 65, 831–848. [Google Scholar] [CrossRef]
  111. Schmidt, M.; Babcock, L.; Kurowski, B.G.; Cassedy, A.; Sidol, C.; Wade, S.L. Usage patterns of an mHealth symptom monitoring app among adolescents with acute mild traumatic brain injuries. J. Head Trauma Rehabil. 2022, 37, 134–143. [Google Scholar] [CrossRef]
  112. Fridriksson, J.; Hillis, A.E. Current approaches to the treatment of post-stroke aphasia. J. Stroke 2021, 23, 183–201. [Google Scholar] [CrossRef] [PubMed]
  113. Fasczewski, K.S.; Garner, L.M.; Clark, L.A.; Michels, H.S.; Migliarese, S.J. Medical therapeutic yoga for multiple sclerosis: Examining self-efficacy for physical activity, motivation for physical activity, and quality of life outcomes. Disabil. Rehabil. 2022, 44, 106–113. [Google Scholar] [CrossRef] [PubMed]
  114. Dicé, F.; Auricchio, M.; Boursier, V.; De Luca Picione, R.; Santamaria, F.; Salerno, M.; Valerio, P.; Freda, M.F. Psychological scaffolding for taking charge of intersex/dsd conditions. the joint listening settings. Psicol. Salute 2018, 2018, 129–145. [Google Scholar] [CrossRef]
  115. Caffò, A.O.; Lopez, A.; Spano, G.; Saracino, G.; Stasolla, F.; Ciriello, G.; Grattagliano, I.; Lancioni, G.E.; Bosco, A. The role of pre-morbid intelligence and cognitive reserve in predicting cognitive efficiency in a sample of Italian elderly. Aging Clin. Exp. Res. 2016, 28, 1203–1210. [Google Scholar] [CrossRef] [PubMed]
  116. Martino, M.L.; Picione, R.L.; Lemmo, D.; Boursier, V.; Freda, M.F. Meaning-making trajectories of resilience in narratives of adolescents with multiple sclerosis. Mediterr. J. Clin. Psychol. 2019, 7, 1–25. [Google Scholar] [CrossRef]
  117. Fischbein, R.; Saling, J.R.; Marty, P.; Kropp, D.; Meeker, J.; Amerine, J.; Chyatte, M.R. Patient-reported Chiari malformation type I symptoms and diagnostic experiences: A report from the national conquer chiari patient registry database. Neurol. Sci. 2015, 36, 1617–1624. [Google Scholar] [CrossRef]
  118. Fabricatore, C.; Radovic, D.; Lopez, X.; Grasso-Cladera, A.; Salas, C.E. When technology cares for people with dementia: A critical review using neuropsychological rehabilitation as a conceptual framework. Neuropsychol. Rehabil. 2020, 30, 1558–1597. [Google Scholar] [CrossRef]
  119. Soler, B.; Ramari, C.; Valet, M.; Dalgas, U.; Feys, P. Clinical assessment, management, and rehabilitation of walking impairment in MS: An expert review. Expert. Rev. Neurother. 2020, 20, 875–886. [Google Scholar] [CrossRef]
  120. Arlati, S.; Di Santo, S.G.; Franchini, F.; Mondellini, M.; Filiputti, B.; Luchi, M.; Ratto, F.; Ferrigno, G.; Sacco, M.; Greci, L. Acceptance and usability of immersive virtual reality in older adults with objective and subjective cognitive decline. J. Alzheimer’s Dis. 2021, 80, 1025–1038. [Google Scholar] [CrossRef]
  121. Pourebadi, M.; Riek, L.D. Facial expression modeling and synthesis for patient simulator systems: Past, present, and future. ACM Trans. Comput. Healthc. 2022, 3, 1–32. [Google Scholar] [CrossRef]
  122. Jahn, F.S.; Skovbye, M.; Obenhausen, K.; Jespersen, A.E.; Miskowiak, K.W. Cognitive training with fully immersive virtual reality in patients with neurological and psychiatric disorders: A systematic review of randomized controlled trials. Psychiatry Res. 2021, 300, 113928. [Google Scholar] [CrossRef] [PubMed]
  123. Park, G.; Balcer, M.J.; Colcombe, J.R.; Hasanaj, L.; Joseph, B.; Kenney, R.; Hudson, T.; Rizzo, J.-R.; Rucker, J.C.; Galettta, S.L.; et al. The MICK (Mobile integrated cognitive kit) app: Digital rapid automatized naming for visual assessment across the spectrum of neurological disorders. J. Neurol. Sci. 2022, 434, 120150. [Google Scholar] [CrossRef] [PubMed]
  124. Plow, M.; Mangal, S.; Geither, K.; Golding, M. A scoping review of tailored self-management interventions among adults with mobility impairing neurological and musculoskeletal conditions. Front. Public Health 2016, 4, 165. [Google Scholar] [CrossRef] [Green Version]
  125. Lopez, A.; Caffò, A.O.; Tinella, L.; Bosco, A. The four factors of mind wandering questionnaire: Content, construct, and clinical validity. Assessment 2021, 30, 433–447. [Google Scholar] [CrossRef] [PubMed]
Table 1. Reviewed studies arranged in alphabetical order.
Table 1. Reviewed studies arranged in alphabetical order.
AuthorsParticipantsAgesGroupTechnologyMain Outcomes
Arroyo-Ferrer et al. [36]120VREEG neurofeedbackBenefits in divided and sustained attention
Bekkers et al. [37]12160–90VRVirtual treadmill Postural stability
Bertomeu-Motos et al. [38]822–58ATMultimodal interfacePositive achievement of cognitive tasks in the experimental group
Calabrò et al. [39] 2260–73VRComputer-assistedGait stability
Capodieci et al. [40]425–11TRComputerized training Accuracy in dictation, reading, inhibition, and working memory test
Eilam-Stock et al. [41]129TRDeep stimulation Improvement in attention and working memory
Gerber et al. [42] 15 43–63TR Interface with Hierarchical StructureParticipants’ Enjoyment and Approval
Jamali et al. [43]434–12TRCoaching technologyImprovement in occupational performance
Jordan et al. [44]419–13ATKeyboard emulatorLiteracy access and words prediction
Lancioni et al. [45]638–59ATSmartphone and adapted softwareCommunication and leisure opportunities
Leonardi et al. [46]3050–65VRRehabilitation systemMood Improvement
Visuo-spatial skills enhancement
Maier et al. [47]3045–75VRAdaptive conjunctive cognitive trainingImprovement in attention, spatial awareness, and cognitive functions
Pinter et al. [48]1432–40TREEG neurofeedbackCognitive improvement correlated with increased functional connectivity
Stasolla et al. [49]107–10ATMicroswitchesImprovement of adaptive skills and positive participation
Stasolla et al. [50]514–18ATMicroswitchesEnhancement of academic performance and personal needs communication
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Stasolla, F.; Lopez, A.; Akbar, K.; Vinci, L.A.; Cusano, M. Matching Assistive Technology, Telerehabilitation, and Virtual Reality to Promote Cognitive Rehabilitation and Communication Skills in Neurological Populations: A Perspective Proposal. Technologies 2023, 11, 43. https://doi.org/10.3390/technologies11020043

AMA Style

Stasolla F, Lopez A, Akbar K, Vinci LA, Cusano M. Matching Assistive Technology, Telerehabilitation, and Virtual Reality to Promote Cognitive Rehabilitation and Communication Skills in Neurological Populations: A Perspective Proposal. Technologies. 2023; 11(2):43. https://doi.org/10.3390/technologies11020043

Chicago/Turabian Style

Stasolla, Fabrizio, Antonella Lopez, Khalida Akbar, Leonarda Anna Vinci, and Maria Cusano. 2023. "Matching Assistive Technology, Telerehabilitation, and Virtual Reality to Promote Cognitive Rehabilitation and Communication Skills in Neurological Populations: A Perspective Proposal" Technologies 11, no. 2: 43. https://doi.org/10.3390/technologies11020043

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop