Maria Grazia Maggio1†
Daniela De Bartolo2,3†
Rocco Salvatore Calabrò1*
Irene Ciancarelli4
Antonio Cerasa5,6,7
Paolo Tonin6
Fulvia Di Iulio3
Stefano Paolucci3Gabriella Antonucci8
Giovanni Morone4,9
Marco Iosa3,8- 1IRCCS Centro Neurolesi “Bonino Pulejo”, Messina, Italy
- 2Department of Human Movement Sciences, Faculty of Behavioral and Movement Sciences, Amsterdam Movement Sciences & Institute for Brain and Behavior Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- 3IRCCS Santa Lucia Foundation, Rome, Italy
- 4Department of Life, Health and Environmental Sciences, University of L’Aquila, L’Aquila, Italy
- 5Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy, Messina, Italy
- 6S’Anna Institute, Crotone, Italy
- 7Pharmaco Technology Documentation and Transfer Unit, Preclinical and Translational Pharmacology, Department of Pharmacy, Health Science and Nutrition, University of Calabria, Rende, Italy
- 8Department of Psychology, Sapienza University of Rome, Rome, Italy
- 9San Raffaele Institute of Sulmona, Sulmona, Italy
Background and aim: Advances in computing technology enabled researchers and clinicians to exploit technological devices for cognitive training and rehabilitation interventions. This expert review aims to describe the available software and device used for cognitive training or rehabilitation interventions of patients with neurological disorders.
Methods: A scoping review was carried out to analyze commercial devices/software for computerized cognitive training (CCT) in terms of feasibility and efficacy in both clinical and home settings. Several cognitive domains responding to the different patients’ needs are covered.
Results: This review showed that cognitive training for patients with neurological diseases is largely covered by several devices that are widely used and validated in the hospital setting but with few translations to remote/home applications. It has been demonstrated that technology and software-based devices are potential and valuable tools to administer remotely cognitive rehabilitation with accessible costs.
Conclusion: According to our results, CCT entails the possibility to continue cognitive training also in different settings, such as home, which is a significant breakthrough for the improvement of community care. Other possible areas of use should be the increase in the amount of cognitive therapy in the free time during the hospital stay.
Highlights
- Devices and software for cognitive rehabilitation are a feasible solution with increasing attention also thanks to the distancing of the COVID-19 pandemic.
- With these devices, different cognitive domains can be trained in the hospital or at home.
- These devices and software can guarantee continuity of care between hospital and home even though the same user interfaces.
- It is necessary to overcome problems of various kinds that limit the spread of these devices: geographical and socio-economic barriers.
1. Introduction
In recent years, the aging of population in the industrialized countries increased the demand for care services, including neurorehabilitation (1, 2). Then, the overload of healthcare systems and the difficulties in organizing services have required the implementation of new methodologies for rehabilitation (2). COVID-19 has affected rehabilitation processes, especially in neurological patients, harming the quality of life of both patients and their families. To face this unexpected pandemic, new innovative models of rehabilitation service have emerged. At the same time, it led to the increase of non-hospital services to guarantee the continuity of care, thanks to technological innovations (3). Moreover, rapid advances in computing technology have enabled researchers to carry out cognitive training and rehabilitation interventions with the assistance of technology (4).
Cognitive rehabilitation (CR) aims to improve residual neuropsychological capacities through specific strategies based on cognitive models. In particular, the innovative techniques mediated by personal computers (PC) use multimedia and computer resources, through hardware and software systems, to implement cognitive functioning, including attention, memory, problem-solving, language, and executive functions (5–8). Computerized methods are based on repeated training of specific cognitive domains, through the execution of tasks involving specific skills. Most of the tools use audio-video feedback as a motivational stimulus. Furthermore, these tools allow modifying the type, duration, and difficulty of the tasks to adapt the intervention to the individual abilities. The exercises are grouped according to the cognitive domain stimulated and adapt to the patient’s abilities to avoid frustration due to too complex or too simple tasks (5). These devices could offer some therapeutic possibilities for the CR of various neurological diseases (4–8). It has been shown that innovative tools, such as PC-based treatments, could facilitate patient management in the rehabilitation process, allowing continuity of care at home through the telerehabilitation mode (9–13). The tools could support restorative training on cognitive functions thanks to the simulation of different cognitive domains, with positive repercussions on the patient’s motivation (4–8). Various authors have also shown that telerehabilitation can improve various cognitive domains, with results comparable to those of conventional face-to-face rehabilitation (3, 10). Despite the many advantages of PC-based approaches, these devices have also some limitations such as: (a) visual interface limiting their use; (b) access prerequisites (i.e., computer skills); (c) lack of acceptability due to photosensitivity problems; (d) acceptability of devices; (e) reliability (some systems have been validated for the clinic but not remotely, or not validated on some types of the population); (f) availability (some systems are too expensive for a patient to maintain or purchase). However, there are neither clear indications nor warnings for the use of these tools, as rigorous comparisons of technical devices in different neurological populations have never been performed.
In this review, we sought to provide an overall picture of the devices on the market that can be used for CR. Furthermore, a secondary aim is to report on the strengths and weaknesses of the devices employed in inpatient or remote/home settings for continuing the rehabilitation process.
2. Search strategy
This scoping review was conducted by searching peer-reviewed articles published between 01 June 2010 and 31 December 2022 using the following databases: PubMed, Embase, Cochrane Database, and Web of Science. The aim of the search strategy was twofold: (1) to track progress in the use of software and device-based technology in terms of technological content, human-machine interaction, and cognitive domain training, and (2) to check neurological populations in which the devices and software for cognitive neurorehabilitation are used. To this end, a comprehensive search was carried out using the search terms: (“Cognitive Rehabilitation” OR “Computer-based” OR “Telerehabilitation”) AND (“Stroke” OR “Traumatic Brain Injury” OR “Dementia” OR Multiple Sclerosis” OR “Parkinson” OR “Rehabilitation”). After the removal of the duplicates, all articles were evaluated based on the titles and abstracts. The inclusion criteria were: (i) patients with neurological disease; (ii) a computerized approach applied to cognitive rehabilitation; (iii) English language; and (v) published in a peer-reviewed journal. We excluded articles that described theoretical models, methodological approaches, algorithms, basic technical descriptions, and validation of experimental devices providing no clear translation to clinical practice. Furthermore, we excluded: (i) animal studies; (ii) conference proceedings, or reviews; (iii) studies focusing only on other innovative approaches (such as virtual reality, exergaming, or serious games), (iv) cognitive remediation relating to physiological condition (i.e., the developmental stage or the elderly), (v) study concerning the mobile-app device, which is too far from the traditional CR program.
The list of articles was then refined based on relevance and summarized according to the inclusion/exclusion criteria. Furthermore, to ensure a greater homogeneity in the results, after the removal of duplicates, the articles were evaluated on the basis of the titles and abstracts by two independent researchers (DDB and MGM). These researchers read the full text of articles suitable for the study and performed the data collection to reduce the risk of bias (i.e., language bias; publication bias; time-lag bias). In case of disagreement on the inclusion and exclusion criteria, the final decision was made by two senior investigators (RSC and GM).
Data extraction was performed on 190 articles. Data were considered for the following information: year and type of publication (e.g., clinical studies, pilot study), characteristics of the participants involved in the study, and purpose of the study (Figure 1). After a thorough review of the complete manuscripts, 34 studies articles met the exclusion/exclusion criteria (Table 1). We reported as a primary outcome the one identified by the researchers, for each study, as between-group (in RCT design) or within-subjects difference (for studies with only one group) on the first-level test, and secondary outcome as differences within groups (for RCT) or on second-level tests (for single-group studies). For every study, we selected only significant results adjusted for multiple comparisons.
Figure 1. Schematic representation of the clinical use of the different cognitive devices and software. The figure illustrates the different devices and cognitive functions trained in the different pathologies and in which setting they were used (home or in hospital). The figure reflects the fields of application as evident from the literature selected in this review.
Table 1. List of devices and their main characteristics, main studies, and clinical populations on which they have been tested.
3. Results
Although our research in PubMed, Embase, Cochrane Database, and Web of Science has found many technological devices used in CR, only the 10 most cited devices were selected. The information obtained from the selection of studies was organized in two tables. Table 1 reports the list of devices and their main characteristics, as well as the studies and clinical populations on which they were tested. Table 2, indeed, shows what type of study was carried out, how the device was used, and what the results are in terms of treatment efficacy. Most of the selected devices (6 out 11) are supplied in software mode. Then, they can be used by purchasing a stand-alone license, which has a limited duration to the subscription chosen on the manufacturer’s website. Of these, only CogMED (14–19) provides a special license for its usage and a specific training for the online tutor. Three devices (Lumosity, Brain HQ, Brain Gymmer) are either available as PC software or can be installed as an app on tablet/phone devices. Finally, only one represents a telerehabilitation platform (NeuroPersonalTrainer) that allows patients to carry out Hospital and home rehabilitation. In most studies included in Table 2, these devices were tested to evaluate their effectiveness compared to conventional rehabilitation. This review reveals that cognitive training for patients with neurological diseases is covered by several efficient devices that are widely used and validated in the hospital setting, but with few translations in remote applications.
Table 2. Performance results reported in the selected studies of this review.
3.1. CogMED
CogMed (QM Training, Pearson Company, Stockholm, Sweden, 2011) is a computer-based software system for training of attention and working memory (WM). CogMed can be accessed via computer and tablet with speakers, and it is mainly used online through the Cogmed website. It has been shown that this device could have positive effects on the rehabilitation of WM. Akerlund et al. carried out a randomized study of 47 patients with acquired brain injury (ABI) in the subacute phase. The authors demonstrated that the device not only improved WM but also cognition and psychological health (14), as well as activity of daily living, as reported also by Johansson & Tornmalm (15). According to these results, Lundqvist et al. (16) performed a cross-over design controlled experimental study using CogMed software on 21 subjects with ABI. They observed significant improvement in WM tasks, occupational performance, performance satisfaction, and overall health rating (16). Svaerke et al. found similar results in a randomized study of 72 patients (17). Moreover, these findings could be generalized to the life context, as suggested by Johansson et al. (15). Improvements have been observed also in other neurological populations, including multiple sclerosis (MS) (18). A study on older adults with Mild Cognitive Impairment (MCI) showed an improvement in cognitive skills, especially in information processing speed and WM, after specific home interventions (51).
On the other hand, Nyberg et al. (19) conducted a study in 26 stroke patients trained with CogMed for 6 weeks. The authors found changes in performance related to the trained computerized task, but no microstructural changes in white matter between rest and training condition (p = 0.99).
3.2. Lumosity™
Lumosity™ (Brain Games Lumos Labs. Lumosity: Recover Your Brain™. San Francisco, Calif.: Dakim, Inc.; 2010) is a CR software that provides access to games to improve cognitive processing speed, flexibility, attention, memory, and problem-solving skills. However, the results of its effectiveness are conflicting.
Withiel et al. found a good usability of the device, especially for its playful aspects, but without improvements in daily memory, in 20 stroke survivors (20). These results were confirmed by Wentink et al. that carried out an experimental study on chronic stroke patients. The authors showed no effect of training on cognitive functioning, QoL, or self-efficacy regarding the control condition, except for minimal effects on WM and speed (21).
Conversely, Stuifbergen et al. performed a study on 183 MS patients, noting that the device is feasible with promising effects in improving cognitive functioning (22). Furthermore, Zickefoose et al. evaluated whether the treatment of severe traumatic brain injury (TBI) survivors could be generalized to comparable, untrained tasks. They found that participants made significant improvements but with limited generalization (23). Finally, evidence is inconsistent regarding the effectiveness of this device on subjective or objective memory or other cognitive components.
3.3. BrainHQ
BrainHQ (Posit Science Corporation, San Francisco CA, 2015) is an online cognitive training system for cognitive exercises. Each user can be monitored throughout the entire training, which is automatically modified according to the skill level reached. BrainHQ has multiple exercises for training different cognitive skills, including attention, speed, memory, sociability, orientation, and intelligence. The program appears to have good feasibility and effective results in CR.
Yeh et al. performed a randomized controlled trial to evaluate the efficacy of a combination of aerobic exercise and cognitive training using BrainHQ on stroke survivors. The authors noticed that, compared to the control group, the experimental group significantly improved global cognitive functioning and memory scores after training (24). These results were confirmed by Charvet et al. (25) in patients with MS, demonstrating how home computer-based cognitive training can improve cognitive functioning. Furthermore, they observed that this telerehabilitation approach enabled good patient compliance and rapid recruitment (25). Finally, an interesting pilot study by O’Neil-Pirozzi et al. explored the feasibility and effects of participating in a computerized cognitive fitness exercise program on ABI adults with positive results (26).
3.4. Neuro PersonalTrainer®
Neuro PersonalTrainer®-MH (GNPT®, Guttmann Institute, Badalona, Spain, 2011) is a module for neurocognitive rehabilitation provided by a computerized tele-rehabilitation platform. It allows one to carry out cognitive training in an intensive and personalized mode (27). Gil-Pages et al. in their cross-over, randomized, controlled, double-blind clinical study observed that chronic stroke patients with cognitive impairment may benefit from cognitive training using this innovative tool (27). On the contrary, Aparicio-López et al., in a randomized clinical trial of 28 stroke patients, found no statistically significant differences when comparing patients using the Neuropersonal-Trainer to those receiving traditional pc-based rehabilitation (52).
3.5. ERICA
ERICA (Giunti Psychometrics, Italy, 2013) is a tool composed of a series of computerized exercises for cognitive rehabilitation. These exercises are dedicated to the rehabilitation of specific skills, such as attention, spatial cognition, memory, verbal executive functions, and non-verbal executive functions, and can be used in patients with neuropsychological deficits resulting from brain injury, developmental disorders, degenerative pathologies, and psychiatric pathologies. The studies using this device mainly involve subjects suffering from Parkinson’s disease (PD), and multiple sclerosis (MS). DeLuca et al. (28) performed a randomized clinical study on 70 PD patients, noting significant improvement after CR in both groups. However, the group receiving the Erica training achieved greater outcomes, especially in attention, orientation, and visuospatial domains (28). The same research group observed similar significant improvements in people with MS (29). The positive effects of Erica on the emotional, motor, and cognitive aspects in MS patients were also highlighted by Barburulo et al. in a study of 63 MS patients (30).
3.6. CogniPlus
CogniPlus (Schuhfried GmbH, Vienna, Austria, 2008) is a tool related to the Vienna Test System, which integrates the diagnosis, treatment, and assessment of various cognitive functions, such as attention, executive functions, memory, spatial processing, and visuomotor abilities. Cogniplus has been shown to be effective in CR. Hagovská et al. (31) performed a study to compare the effectiveness of two types of cognitive training in 60 older adults with MCI. The results showed that although both traditional and experimental groups had an improvement, the Cogniplus group reported better scores in quality of life and better attention (31).
Cogniplus is also effective in combination treatments. Westerhof-Evers et al. (53) conducted a study to evaluate the effects of treatment using Cogniplus combined with T-scEmo (a tool that affects emotions) on social cognition and emotion regulation in 61 TBI patients. The authors noticed that this combined approach may be effective in rehabilitating impairments in social cognition (53). Another study by Hagovská et al. (54) on 80 elderly participants with MCI showed that Cogniplus can improve balance control, cognitive functions, gait speed, and activities of daily living, when combined to motor interventions (54).
In contrast to these studies, Zimmerman et al. (32) performed a study on patients with Parkinson’s disease (PD) using cognitive training with Cogniplus and motor training with a movement game in different groups. They found that specific computer training for cognition is not superior to a motion-controlled computer game in improving cognitive performance (32).
3.7. Attention process training
APT (Lash & Associates Publishing/Training Inc, Youngsville, North Carolina, 2010) is a clinical program used for attention process training in adolescents, adults, and older adults with ABI. It was developed by Sohlberg & Mateer, and it is based on scientific evidence, as it has demonstrated its effectiveness in the rehabilitation of patients with cognitive disorders (55).
Pantoni et al. (33) carried out a single-blind randomized clinical trial to evaluate the effects of CR in 46 patients with MCI, using the Attention Process Training (APT) program. The authors found that APT potentially enhances focused attention and WM and appears to increase activity in brain circuits involved in cognition (33). APT training also seems to be effective in other patient populations. Walton et al. (34) carried out a randomized study of 65 PD patients to evaluate whether targeted training could improve freezing and executive dysfunction. The results highlighted that APT training can be an effective method to improve processing speed and reduce daytime sleepiness (34).
3.8. CoTras
The CoTras program (RPIO Co., Ltd., Geumcheon-gu, Seoul, 2010) is a computer-based cognitive rehabilitation device. It consists of real-life training content which is defined according to the environment in Korea. It has several exercises that adapt to the patient’s cognitive abilities, including difficulty, time, and speed of exercise execution. Park and Park (35) carried out a study to investigate the effects of CoTras on cognition in thirty acute stroke patients. The results showed that the tool can stimulate the recovery of global cognitive function, with regard to and visual perception (35).
3.9. BrainGymmer
BrainGymmer (Dezzel Media, The Netherlands, 2010) consists of computer-based cognitive training exercises via a website. The training tasks consist of games designed to be challenging and customized to the characteristics of the user.
Van de Ven et al. (36) carried out a double-blind, randomized controlled trial to investigate whether the computer-based training improves executive functioning after stroke. The results showed that patients submitted to Braingymmer training had the same improvement in executive and general cognitive functioning as control groups. This improvement was likely due to non-specific training effects. Therefore, the Braingymmer program does not seem to make significantly different improvements compared to conventional methods. Nevertheless, other studies on larger samples should be implemented to ascertain the effectiveness of this tool.
3.10. RehaCom®
RehaCom (HASOMED GmbH, Magdeburg, Germany, 1997) is a software for computer-assisted cognitive rehabilitation useful in the management of different cognitive disorders. The system supports recovery and replacement processes, potentiating cognitive strategies and offering targeted therapeutic solutions for rehabilitation.
Various studies have shown positive results of intervention using Rehacom, even in telerehabilitation modality, to improve or stabilize cognitive decline. Nousia et al. (37) carried out a study on 46 Greek patients with MCI. The authors demonstrated the efficacy of Rehacom on delayed and semantic memory, word recognition, and attentional shifting. The results have been confirmed by other authors. Naeeni Davarani et al. (38) investigated the effect of RehaCom on attention, response control, processing speed, working memory, visuospatial skills, and verbal/nonverbal executive functions in 60 MS patients. They observed that RehaCom treatment improved all cognitive functions, and this effect was maintained over time (i.e., at three-month follow-up) (38). Moreover, Amir et al. (39) carried out a study of 50 stroke survivors. They showed a significant improvement in working memory and processing speed in the experimental group compared to the control group after a 5-week training with the software (39). These results were confirmed by Messinis et al. (40), who carried out a randomized controlled study to examine the efficacy of at-home intervention using RehaCom software in 36 patients with secondary progressive MS. The authors found that the tool can be effective in improving cognitive functioning and mood with positive results on fatigue and health-related quality of life (40). These findings were confirmed by a multicenter study carried out by the same authors (41) on 58 MS patients. In fact, the authors showed significant improvements in episodic memory, information processing speed/attention, and executive functions with a positive perception of patients in using the training software RehaCom (41). Moreover, Campbell et al. (42) explored the efficacy of home-based computer-aided cognitive rehabilitation in 38 patients with MS using neuropsychological assessment and advanced structural and functional MRI. The treatment group had greater activation in the bilateral prefrontal cortex and right temporoparietal regions. In addition, improved cognitive performance was noted in patients treated with Rehacom (42). Finally, Bonavita et al. (43) performed a study on 18 relapsing–remitting MS patients treated with Rehacom software. They demonstrated that training with the software can induce an adaptive cortical reorganization as well as better cognitive performance (43).
Darestani et al. (44) conducted research to investigate the effect of RehaCom treatment on verbal performance in 60 MS patients. The results showed that treatment with the software can improve speech fluency, verbal learning, and memory in MS patients (44).
Veisi-Pirkoohi et al. (45) found that RehaCom rehabilitation software was effective on ADL, attention, and response control in 50 chronic stroke patients due to middle and anterior cerebral arteries occlusion (45). Yoo et al. (7), in their study on 46 patients with stroke, found that computer-assisted cognitive rehabilitation with the RehaCom program improved cognitive function. This raises the idea that the tool may be helpful for stroke patients who have cognitive impairment (7). Fernández et al. (46) investigated the effectiveness of the software on patients with ABI. The authors showed a good efficacy of the training procedure in focused attention, digit span, and logical and working memory (46). In another study performed on 50 hospitalized patients (56), the same authors found an improvement in the trained functions in all patients. However, adverse effects, including mental fatigue, headaches, and eye irritation, have been found to negatively affect the usability of the tool (56).
Finally, an interesting randomized controlled trial (47) was carried out on 8 patients with PD. The authors found that the patients improved attention and processing speed with changes in neural plasticity, as investigated by fMRI (47).
3.11. GRADIOR
GRADIOR (INTRAS Foundation, Spain) is a multimedia software for cognitive stimulation, neuropsychological assessment, and rehabilitation. It consists of personalized exercises that train various cognitive domains, such as attention, memory, orientation, calculation, perception, reasoning, and language. This software creates a multimedia environment with high flexibility and demanding challenges that boost the cognitive components. The use of the software requires the presence of a qualified therapist to support the user during the assessment and training. Few studies have evaluated its usability and effectiveness in the rehabilitation field. Diaz Baquero et al. performed an RCT study on 43 patients with MCI and mild dementia, highlighting good adherence to treatment, good acceptability, and potential efficacy of the device (8). Another RCT performed by the same authors on 89 people with MCI and dementia demonstrated the benefit of this training on several cognitive domains (48). These promising results were confirmed by Gongora Alonso et al., who observed good acceptability of the tool in patients with severe and prolonged mental illness (49). Finally, Vanova et al. performed an RCT of 400 people with MCI and mild dementia treated with Gradior. They found significant improvements in most patients, with long-term maintenance of the results (50).
4. Discussion
This review aimed to identify suitable technological devices for the CR of chronic neurological patients. Specifically, our literature research has shown how these devices can be used with different neurological pathologies, including stroke, MS, TBI and PD. In detail, it emerges that the clinical population with the most trials is stroke (N = 10) (19–21, 24, 29, 35, 36, 39, 45, 52), followed by MS (N = 9) (18, 22, 25, 30, 38, 40–43), PD (N = 4) (28, 32, 34, 47), and traumatic and acquired brain injury (N = 4, respectively) (14, 17, 23, 26). Only two studies investigating patients with different neurological pathologies were recruited (15, 16), as reported in Table 2. The misrepresentation of RCT studies with such a different neurological population could be intrinsically linked to the difficulty in managing patients affected, e.g., by TBI and dementia. For dementia, there is some evidence that computer-based cognitive rehabilitation may be of help in improving different cognitive domains (57). In particular, the software “GRADIOR” looks promising in the CR field (8, 48–50). Moreover, previous studies have applied computerized approaches using photos of the patient and his/her personal surroundings, with positive results (58, 59). However, these studies were excluded for temporal reasons.
Moreover, most studies reported a statistically significant efficacy of using the PC-based devices in reparative CR. However, only in some cases they were superior to conventional treatments. Training duration, frequency and timing is still unclear. For CogMed, 5 weeks of intervention with each session lasting between 30 and 45 min seems to be the best solution for different populations of patients. However, the efficacy was mainly observed within the group, and not with respect to the control group (14–19). Luminosity™ was mainly used in patients with stroke, but also with MS and TBI, with an intervention duration ranging from 4 to 12 weeks (each session lasting from 20 to 45 min) and with an efficacy higher than that observed for the control group (20–23). Brain HQ needed longer intervention times (12–20 weeks, each session lasting 30–60 min), but with a higher efficacy reported with respect to control intervention for patients with stroke, MS and TBI (24–26). Three studies investigated the use of ERICA for 8–24 weeks (each session 45–60 min), demonstrating significant results only within the experimental group (28–30). CogniPlus (31, 32) and APT (33, 34) have been used for patients with MCI or PD with high variability in the duration of interventions. CoTras (35) and BrainGymmer (36) were both tested in a single study on patients with stroke, the former for a shorter period (4 vs. 12 weeks) and with between group significant differences.
RehaCom was the device more widely tested, especially in patients with MS. The high number of studies increased the variability of the adopted protocols, with a duration of the intervention going from 5 to 15 weeks (each session ranged between 30 and 60 min). However, literature on this device reports solid statistically significant results about its efficacy also when compared to conventional interventions (7, 37–47).
Therefore, we noticed that the importance of training cognitive functions is increasingly evident in the literature, also for facilitating learning processes in motor recovery (4, 60). Indeed, computerized cognitive rehabilitation has proven effective in combination with other methods. A practical example can be the application of acupuncture coupled to transcranial direct current stimulation with computerized cognitive rehabilitation. This method showed good results in cognitive performance in individuals with vascular cognitive impairment (61) and people with stroke (62). A recent study by Shaker et al. (62) demonstrated significant improvement in scores of attention and concentration domains, figural memory, logical reasoning, and reaction times performance. People with cognitive disabilities are treated intensively in the subacute stage of the disease, while unfortunately, they have little access to treatment in the chronic stage. This problem is due to the burden of the Local Health Care Institutions. The underestimation as well as the reduced possibility of effective cognitive training after subacute rehabilitation regards both subjects with central nervous system pathology and those affected by other conditions, such as for example non-CNS cancer (63). This is why new solutions, including telemedicine and home devices/software for cognitive rehabilitation, may be helpful to guarantee the continuity of care. In fact, they should be used when geographical and socio-economic barriers prevent the patient from reaching primary clinics. This will allow each patient to receive monitoring and rehabilitation, through remote devices (3, 64–66).
Moreover, the patient’s perception of the device usability is a key point of rehabilitation. In fact, recent studies have pointed out that the adaptability of technology also includes adapting to patients’ emotions or perceptions. An interesting study by Norman et al. (65) pointed out that perception of a device influences the use of that device itself (37). Nonetheless, this aspect deserves further investigation, as some tools could have high costs and reduce the possibility of customizing the design of the tools. Although in the last period very flexible low-cost proposals have been advanced, also based on smartphones and apps to download for free. We have not explored this field as they are out of the scope of this review. Possible problems concerning the diffusion and use of such devices at home could concern: (i) the absence of a caregiver to supervise the training, especially for patients with greater impairment and with a greater need for therapy; (ii) the lack of experience with technological interfaces and PCs by both patient and caregiver; (iii) the lack of structural technical requirements such as not having a PC or an internet connection. Similar issues have recently been raised by Mantovani et al. (67) concerning the use of Virtual Reality as a home therapy for CR.
In general, it seems that the use of technological devices for CR is promising, but with inconsistencies due to the variations in study design. However, we must bracket the proposal with a caveat. Although these technological devices have features that make them highly adaptive to the patient’s performance. For more severe and subacute subjects they cannot replace conventional CR, in which neuropsychologists and speech therapists play a fundamental role. In fact, their optimal use always remains integrated with conventional CR, or they are part of a rehabilitation process following discharge, to support the patient remotely. However, the protocols of the various studies are very different both in the frequency and the duration of the sessions. This makes it difficult to judge the effectiveness of the tool, so new randomized trials with large samples should be conducted to confirm this aspect. Moreover, it is important that clinicians are familiar with the different devices, in order to facilitate the selection of the appropriate device for the treatment to be performed. Finally, another problem is related to the difficulty of standardizing tests for patients with different neurological pathologies. This implies the need to validate tests for different patients, favoring the continuous updating of devices and tests. Indeed, young subjects, such as those with MS, are more familiar with computerized devices and may require different tests than patients with MCI. Often young patients stop testing because they get bored, or quickly reach the various levels of the tests. On the other hand, patients with dementia and severe cognitive decline may have serious difficulties in using the devices. This could be the main reason why we did not find studies in patients with dementia.
Our review had the ambitious aim of offering an overview of the devices currently in use in clinical practice for the computerized CR of neurological patients. We have collected many studies with the aim of describing the devices and highlighting their strengths and weaknesses. A wide variability among the revised papers was noted in terms of primary as well as secondary outcome measures, even when aiming at measuring the same cognitive domain. This is accompanied by a wide variability also in the duration of treatments, including both session duration and length of rehabilitative period in which a specific device was used (as shown in Table 2). There is the need to standardize assessment and rehabilitative protocols by identifying the key parameter for each device. The inter-rater reliability in the coding and interpretation of these parameters, which in this review cannot be performed given the wide variability among the studies. Thus, this work has limitations. Unlike validation studies, it is not possible to operationalize and define the key parameters being analyzed in the identified literature and then demonstrate inter-rater reliability in the coding or interpretation of each of the defined parameters. The scientific literature on this topic is very varied: different devices are used, for different types of patients, administering a different amount of therapies/duration. Further meta-analysis reviews are needed to fulfill this purpose. In the near future, various factors can consolidate and improve the possibility of carrying out cognitive therapy using software and platforms at home. They include: (i) better accessibility (in terms of lower costs and greater geographical coverage), (ii) higher attention to the chronic and territorial phase of neurorehabilitation and (iii) a growing sensitivity to the possibility of ensuring a better quality of life for brain injury survivors.
With this in mind and considering the aforementioned limitations, PC based approaches could be valuable complementary tools to improve cognitive function and partly guarantee the continuity of care in neurological patients.
Author contributions
MM: Conceptualization, Formal analysis, Methodology, Writing – original draft. DB: Formal analysis, Investigation, Methodology, Writing – original draft. RC: Investigation, Methodology, Writing – original draft, Writing – review & editing. IC: Supervision, Validation, Writing – review & editing. AC: Validation, Writing – review & editing, Supervision. PT: Supervision, Visualization, Writing – review & editing. FI: Software, Validation, Writing – review & editing. SP: Software, Writing – review & editing. GA: Software, Validation, Writing – review & editing. GM: Methodology, Visualization, Writing – review & editing. MI: Supervision, Visualization, Writing – review & editing.
Acknowledgments
We thank the University of Catania, as MM performed this study during her PhD in Neuroscience. The authors wish to thank Andrew De Marco for English editing.
Conflict of interest
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.
The reviewer MA declared a shared affiliation with the author GA to the handling editor at the time of review.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
1. Md Fadzil, NH, Shahar, S, Rajikan, R, Singh, DKA, Mat Ludin, AF, Subramaniam, P, et al. A scoping review for usage of Telerehabilitation among older adults with mild cognitive impairment or cognitive frailty. Int J Environ Res Public Health. (2022) 19:4000. doi: 10.3390/ijerph19074000
2. Xiang, YT, Zhao, YJ, Liu, ZH, Li, XH, Zhao, N, Cheung, T, et al. The COVID-19 outbreak and psychiatric hospitals in China: managing challenges through mental health service reform. Int J Bio Sci. (2020) 16:1741–4. doi: 10.7150/ijbs.45072
3. Maggio, MG, De Luca, R, Manuli, A, and Calabrò, RS. The five 'W' of cognitive telerehabilitation in the COVID-19 era. Expert Rev Med Devices. (2020) 17:473–5. doi: 10.1080/17434440.2020.1776607
4. Morone, G, Spitoni, GF, De Bartolo, D, et al. Rehabilitative devices for a top-down approach. Expert Rev Med Devices. (2019) 16:187–95. doi: 10.1080/17434440.2019.1574567
5. Vilou, I, Bakirtzis, C, Artemiadis, A, Ioannidis, P, Papadimitriou, M, Konstantinopoulou, E, et al. Computerized cognitive rehabilitation for treatment of cognitive impairment in multiple sclerosis: an explorative study. J Integr Neurosci. (2020) 19:341–7. doi: 10.31083/j.jin.2020.02.35
6. Sampanis, DS, Mevorach, C, Shalev, L, et al. Cognitive deficits after stroke through computerized progressive attentional training (CPAT): a pilot study. Phys Med Rehabil Int. (2015) 2:1058.
7. Yoo, C, Yong, MH, Chung, J, and Yang, Y. Effect of computerized cognitive rehabilitation program on cognitive function and activities of living in stroke patients. J Phys Ther Sci. (2015) 27:2487–9. doi: 10.1589/jpts.27.2487
8. Diaz Baquero, AA, Perea Bartolomé, MV, Toribio-Guzmán, JM, Martínez-Abad, F, Parra Vidales, E, Bueno Aguado, Y, et al. Determinants of adherence to a "GRADIOR" computer-based cognitive training program in people with mild cognitive impairment (MCI) and mild dementia. J Clin Med. (2022) 11:1714. doi: 10.3390/jcm11061714
9. Zhou, X, Snoswell, CL, Harding, LE, Bambling, M, Edirippulige, S, Bai, X, et al. The role of telehealth in reducing the mental health burden from COVID-19. Telemed J E Health. (2020) 26:377–9. doi: 10.1089/tmj.2020.0068
10. De Cola, MC, De Luca, R, Bramanti, A, Bertè, F, Bramanti, P, and Calabrò, RS. Tele-health services for the elderly: a novel southern Italy family needs-oriented model. J Telem Telecare. (2016) 22:356–62. doi: 10.1177/1357633X15604290
11. Burns, SP, Terblanche, M, Perea, J, et al. mHealth intervention applications for adults living with the effects of stroke: a scoping review. Arch Rehabil Res Clin Transl. (2020) 3:100095. doi: 10.1016/j.arrct.2020.100095
12. Meulenberg, CJW, de Bruin, ED, and Marusic, U. A perspective on implementation of technology-driven Exergames for adults as Telerehabilitation services. Front Psychol. (2022) 13:840863. doi: 10.3389/fpsyg.2022.840863
13. Bernini, S, Panzarasa, S, Sinforiani, E, Quaglini, S, Cappa, SF, Cerami, C, et al. HomeCoRe for Telerehabilitation in mild or major neurocognitive disorders: a study protocol for a randomized controlled trial. Front Neurol. (2021) 12:752830. doi: 10.3389/fneur.2021.752830
14. Akerlund, E, Esbjörnsson, E, Sunnerhagen, KS, and Björkdahl, A. Can computerized working memory training improve impaired working memory, cognition and psychological health? Brain Inj. (2013) 27:1649–57. doi: 10.3109/02699052.2013.830195
15. Johansson, B, and Tornmalm, M. Working memory training for patients with acquired brain injury: effects in daily life. Scand J Occup Ther. (2012) 19:176–83. doi: 10.3109/11038128.2011.603352
16. Lundqvist, A, Grundström, K, Samuelsson, K, and Rönnberg, J. Computerized training of working memory in a group of patients suffering from acquired brain injury. Brain Inj. (2010) 24:1173–83. doi: 10.3109/02699052.2010.498007
17. Svaerke, K, Pyke, SB, Tjoernlund, M, Humle, F, and Mogensen, J. Effects of computer-based cognitive rehabilitation on working memory in patients with acquired brain injury in the chronic phase, a pilot-study. Brain Inj. (2022):1–11. doi: 10.1080/02699052.2022.2034965
18. Blair, M, Goveas, D, Safi, A, Marshall, C, Rosehart, H, Orenczuk, S, et al. Does cognitive training improve attention/working memory in persons with MS? A pilot study using the Cogmed working memory training program. Mult Scler Relat Disord. (2021) 49:102770. doi: 10.1016/j.msard.2021.102770
19. Nyberg, CK, Nordvik, JE, Becker, F, Rohani, DA, Sederevicius, D, Fjell, AM, et al. A longitudinal study of computerized cognitive training in stroke patients – effects on cognitive function and white matter. Top Stroke Rehabil. (2018) 25:241–7. doi: 10.1080/10749357.2018.1443570
20. Withiel, TD, Wong, D, Ponsford, JL, Cadilhac, DA, New, P, Mihaljcic, T, et al. Comparing memory group training and computerized cognitive training for improving memory function following stroke: a phase II randomized controlled trial. J Rehabil Med. (2019) 51:343–51. doi: 10.2340/16501977-2540
21. Wentink, MM, Berger, MA, de Kloet, AJ, Meesters, J, Band, GP, Wolterbeek, R, et al. The effects of an 8-week computer-based brain training programme on cognitive functioning, QoL and self-efficacy after stroke. Neuropsychol Rehabil. (2016) 26:847–65. doi: 10.1080/09602011.2016.1162175
22. Stuifbergen, AK, Becker, H, Perez, F, Morrison, J, Brown, A, Kullberg, V, et al. Computer-assisted cognitive rehabilitation in persons with multiple sclerosis: results of a multi-site randomized controlled trial with six month follow-up. Disabil Health J. (2018) 11:427–34. doi: 10.1016/j.dhjo.2018.02.001
23. Zickefoose, S, Hux, K, Brown, J, and Wulf, K. Let the games begin: a preliminary study using attention process training-3 and Lumosity™ brain games to remediate attention deficits following traumatic brain injury. Brain Inj. (2013) 27:707–16. doi: 10.3109/02699052.2013.775484
24. Yeh, TT, Chang, KC, and Wu, CY. The active ingredient of cognitive restoration: a multicenter randomized controlled trial of sequential combination of aerobic exercise and computer-based cognitive training in stroke survivors with cognitive decline. Arch Phys Med Rehabil. (2019) 100:821–7. doi: 10.1016/j.apmr.2018.12.020
25. Charvet, LE, Yang, J, Shaw, MT, Sherman, K, Haider, L, Xu, J, et al. Cognitive function in multiple sclerosis improves with telerehabilitation: results from a randomized controlled trial. PLoS One. (2017) 12:e0177177. doi: 10.1371/journal.pone.0177177
26. O'Neil-Pirozzi, TM, and Hsu, H. Feasibility and benefits of computerized cognitive exercise to adults with chronic moderate-to-severe cognitive impairments following an acquired brain injury: a pilot study. Brain Inj. (2016) 30:1617–25. doi: 10.1080/02699052.2016.1199906
27. Gil-Pagés, M, Solana, J, Sánchez-Carrión, R, Tormos, JM, Enseñat-Cantallops, A, and García-Molina, A. A customized home-based computerized cognitive rehabilitation platform for patients with chronic-stage stroke: study protocol for a randomized controlled trial. Trials. (2018) 19:191. doi: 10.1186/s13063-018-2577-8
28. De Luca, R, Latella, D, Maggio, MG, et al. Computer assisted cognitive rehabilitation improves visuospatial and executive functions in Parkinson's disease: preliminary results. NeuroRehabilitation. (2019) 45:285–90. doi: 10.3233/NRE-192789
29. De Luca, R, Leonardi, S, Spadaro, L, et al. Improving cognitive function in patients with stroke: can computerized training be the future? J Stroke Cerebrovasc Dis. (2018) 27:1055–60. doi: 10.1016/j.jstrokecerebrovasdis.2017.11.008
30. Barbarulo, AM, Lus, G, Signoriello, E, Trojano, L, Grossi, D, Esposito, M, et al. Integrated cognitive and Neuromotor rehabilitation in multiple sclerosis: a pragmatic study. Front Behav Neurosci. (2018) 12:196. doi: 10.3389/fnbeh.2018.00196
31. Hagovská, M, Dzvoník, O, and Olekszyová, Z. Comparison of two cognitive training programs with effects on functional activities and quality of life. Res Gerontol Nurs. (2017) 10:172–80. doi: 10.3928/19404921-20170524-01
32. Zimmermann, R, Gschwandtner, U, Benz, N, Hatz, F, Schindler, C, Taub, E, et al. Cognitive training in Parkinson disease: cognition-specific vs nonspecific computer training. Neurology. (2014) 82:1219–26. doi: 10.1212/WNL.0000000000000287
33. Pantoni, L, Poggesi, A, Diciotti, S, Valenti, R, Orsolini, S, Della Rocca, E, et al. Effect of attention training in mild cognitive impairment patients with subcortical vascular changes: the RehAtt study. J Alzheimers Dis. (2017) 60:615–24. doi: 10.3233/JAD-170428
34. Walton, CC, Mowszowski, L, Gilat, M, Hall, JM, O’Callaghan, C, Muller, AJ, et al. Cognitive training for freezing of gait in Parkinson's disease: a randomized controlled trial. NPJ Parkinsons Dis. (2018) 4:15. doi: 10.1038/s41531-018-0052-6
35. Park, JH, and Park, JH. The effects of a Korean computer-based cognitive rehabilitation program on cognitive function and visual perception ability of patients with acute stroke. J Phys Ther Sci. (2015) 27:2577–9. doi: 10.1589/jpts.27.2577
36. van de Ven, RM, Buitenweg, JI, Schmand, B, Veltman, DJ, Aaronson, JA, Nijboer, TC, et al. Brain training improves recovery after stroke but waiting list improves equally: a multicenter randomized controlled trial of a computer-based cognitive flexibility training. PLoS One. (2017) 12:e0172993. doi: 10.1371/journal.pone.0172993
37. Nousia, A, Martzoukou, M, Siokas, V, Aretouli, E, Aloizou, AM, Folia, V, et al. Beneficial effect of computer-based multidomain cognitive training in patients with mild cognitive impairment. Appl Neuropsych Adult. (2021) 28:717–26. doi: 10.1080/23279095.2019.1692842
38. Naeeni Davarani, M, Arian Darestani, A, Hassani-Abharian, P, Vaseghi, S, Zarrindast, MR, and Nasehi, M. RehaCom rehabilitation training improves a wide-range of cognitive functions in multiple sclerosis patients. Appl Neuropsychol Adult. (2022) 29:262–72. doi: 10.1080/23279095.2020.1747070
39. Amiri, S, Hassani-Abharian, P, Vaseghi, S, Kazemi, R, and Nasehi, M. Effect of RehaCom cognitive rehabilitation software on working memory and processing speed in chronic ischemic stroke patients. Assist Technol. (2021) 35:41–7. doi: 10.1080/10400435.2021.1934608
40. Messinis, L, Kosmidis, MH, Nasios, G, Konitsiotis, S, Ntoskou, A, Bakirtzis, C, et al. Do secondary progressive multiple sclerosis patients benefit from computer- based cognitive neurorehabilitation? A randomized sham controlled trial. Mult Scler Relat Disord. (2020) 39:101932. doi: 10.1016/j.msard.2020.101932
41. Messinis, L, Nasios, G, Kosmidis, MH, Zampakis, P, Malefaki, S, Ntoskou, K, et al. Efficacy of a computer-assisted cognitive rehabilitation intervention in relapsing-remitting multiple sclerosis patients: a multicenter randomized controlled trial. Behav Neurol. (2017) 2017:1–17. doi: 10.1155/2017/5919841
42. Campbell, J, Langdon, D, Cercignani, M, and Rashid, W. A randomized controlled trial of the efficacy of cognitive rehabilitation in multiple sclerosis: a cognitive, behavioral, and MRI study. Neural Plastic. (2016) 2016:1–9. doi: 10.1155/2016/4292585
43. Bonavita, S, Sacco, R, Della Corte, M, Esposito, S, Sparaco, M, d’Ambrosio, A, et al. Computer-aided cognitive rehabilitation improves cognitive performances and induces brain functional connectivity changes in relapsing remitting multiple sclerosis patients: an exploratory study. J Neurol. (2015) 262:91–100. doi: 10.1007/s00415-014-7528-z
44. Arian Darestani, A, Naeeni Davarani, M, Hassani-Abharian, P, Zarrindast, MR, and Nasehi, M. The therapeutic effect of treatment with RehaCom software on verbal performance in patients with multiple sclerosis. J Clin Neurosci. (2020) 72:93–7. doi: 10.1016/j.jocn.2020.01.007
45. Veisi-Pirkoohi, S, Hassani-Abharian, P, Kazemi, R, Vaseghi, S, Zarrindast, MR, and Nasehi, M. Efficacy of cognitive rehabilitation software RehaCom in activities of daily living, attention and response control in chronic stroke patients. J Clin Neurosci. (2020) 71:101–7. doi: 10.1016/j.jocn.2019.08.114
46. Fernandez, E, Bergado Rosado, JA, Rodriguez Perez, D, Salazar Santana, S, Torres Aguilar, M, and Bringas, ML. Effectiveness of a computer-based training program of attention and memory in patients with acquired brain damage. Behav Sci (Basel). (2017) 8:4. doi: 10.3390/bs8010004
47. Cerasa, A, Gioia, MC, Salsone, M, Donzuso, G, Chiriaco, C, Realmuto, S, et al. Neurofunctional correlates of attention rehabilitation in Parkinson's disease: an explorative study. Neurol Sci. (2014) 35:1173–80. doi: 10.1007/s10072-014-1666-z
48. Diaz Baquero, AA, Franco-Martín, MA, Parra Vidales, E, Toribio-Guzmán, JM, Bueno-Aguado, Y, Martínez Abad, F, et al. The effectiveness of GRADIOR: a neuropsychological rehabilitation program for people with mild cognitive impairment and mild dementia. Results of a randomized controlled trial after 4 and 12 months of treatment. J Alzheimers Dis. (2022) 86:711–27. doi: 10.3233/JAD-215350
49. Góngora Alonso, S, Fumero Vargas, G, Morón Nozaleda, L, Sainz de Abajo, B, de la Torre, DI, and Franco, M. Usability analysis of a system for cognitive rehabilitation, "Gradior", in a Spanish region. Telemed J E Health. (2020) 26:671–82. doi: 10.1089/tmj.2019.0084
50. Vanova, M, Irazoki, E, García-Casal, JA, Martínez-Abad, F, Botella, C, Shiells, KR, et al. The effectiveness of ICT-based neurocognitive and psychosocial rehabilitation programmes in people with mild dementia and mild cognitive impairment using GRADIOR and ehcoBUTLER: study protocol for a randomised controlled trial. Trials. (2018) 19:100. doi: 10.1186/s13063-017-2371-z
51. Wayne, RV, Hamilton, C, Jones Huyck, J, and Johnsrude, IS. Working memory training and speech in noise comprehension in older adults. Front Aging Neurosci. (2016) 8:49. doi: 10.3389/fnagi.2016.00049
52. Aparicio-López, C, García-Molina, A, García-Fernández, J, López-Blázquez, R, Enseñat-Cantallops, A, Sánchez-Carrión, R, et al. Combination treatment in the rehabilitation of visuo-spatial neglect. Psicothema. (2016) 28:143–9. doi: 10.7334/psicothema2015.93
53. Westerhof-Evers, HJ, Visser-Keizer, AC, Fasotti, L, Schönherr, MC, Vink, M, van der Naalt, J, et al. Effectiveness of a treatment for impairments in social cognition and emotion regulation (T-ScEmo) after traumatic brain injury: a randomized controlled trial. J Head Trauma Rehabil. (2017) 32:296–307. doi: 10.1097/HTR.0000000000000332
54. Hagovská, M, and Olekszyová, Z. Relationships between balance control and cognitive functions, gait speed, and activities of daily living. Z Gerontol Geriatr. (2016) 49:379–85. doi: 10.1007/s00391-015-0955-3
55. Sohlberg, MM, and Mateer, CA. Effectiveness of an attention-training program. J Clin Exp Neuropsychol. (1987) 9:117–30. doi: 10.1080/01688638708405352
56. Fernández, E, Bringas, ML, Salazar, S, Rodríguez, D, García, ME, and Torres, M. Clinical impact of RehaCom software for cognitive rehabilitation of patients with acquired brain injury. MEDICC Rev. (2012) 14:32–5. doi: 10.37757/MR2012V14.N4.8
57. Klimova, B, and Maresova, P. Computer-based training programs for older people with mild cognitive impairment and/or dementia. Front Hum Neurosci. (2017) 11:262. doi: 10.3389/fnhum.2017.00262
58. Hofmann, M, Hock, C, Kühler, A, and Müller-Spahn, F. Interactive computer-based cognitive training in patients with Alzheimer's disease. J Psychiatr Res. (1996) 30:493–501. doi: 10.1016/S0022-3956(96)00036-2
59. Hofmann, M, Hock, C, and Müller-Spahn, F. Computer-based cognitive training in Alzheimer's disease patients. Ann N Y Acad Sci. (1996) 777:249–54. doi: 10.1111/j.1749-6632.1996.tb34427.x
60. Park, HK, Song, MK, Kim, JH, and Han, JY. A randomized controlled trial to evaluate the effectiveness and safety of electro acupuncture and transcranial direct current stimulation with computerized cognitive rehabilitation in patients with vascular cognitive impairment. Medicine (Baltimore). (2020) 99:e21263. doi: 10.1097/MD.0000000000021263
61. Jiang, C, Yang, S, Tao, J, Huang, J, Li, Y, Ye, H, et al. Clinical efficacy of acupuncture treatment in combination with RehaCom cognitive training to improve cognitive function in stroke: a factorial 2 × 2 design a randomized controlled trial. J Am Med Dir Assoc. (2016) 17:1114–22. doi: 10.1016/j.jamda.2016.07.021
62. Shaker, HA, Sawan, SAE, Fahmy, EM, Ismail, RS, and Elrahman, SAEA. Effect of transcranial direct current stimulation on cognitive function in stroke patients. Egypt J Neurol Psychiatr Neurosurg. (2018) 54:32. doi: 10.1186/s41983-018-0037-8
63. De Luca, R, Torrisi, M, Bramanti, A, et al. A multidisciplinary telehealth approach for community dwelling older adults. Geriatr Nurs. (2021) 42:635–42. doi: 10.1016/j.gerinurse.2021.03.015
64. Manuli, A, Maggio, MG, Tripoli, D, Gullì, M, Cannavò, A, la Rosa, G, et al. Patients' perspective and usability of innovation technology in a new rehabilitation pathway: an exploratory study in patients with multiple sclerosis. Mult Scler Relat Disord. (2020) 44:102312. doi: 10.1016/j.msard.2020.102312
65. Norman, DA. Emotional design: Why we love (or hate) everyday things. New York: Basic Civitas Books (2004).
66. De Bartolo, D, Morone, G, Lupo, A, et al. From paper to informatics: the post soft care-app, an easy-to-use and fast tool to help therapists identify unmet needs in stroke patients. Funct Neurol. (2018) 33:200–5.
Keywords: rehabilitation software, telerehabilitation, cognitive rehabilitation, computer-based, rehabilitation, executive functions, memory, attention
Citation: Maggio MG, De Bartolo D, Calabrò RS, Ciancarelli I, Cerasa A, Tonin P, Di Iulio F, Paolucci S, Antonucci G, Morone G and Iosa M (2023) Computer-assisted cognitive rehabilitation in neurological patients: state-of-art and future perspectives. Front. Neurol. 14:1255319. doi: 10.3389/fneur.2023.1255319
Edited by:
Teresa Paolucci, University of Studies G. d'Annunzio Chieti and Pescara, ItalyReviewed by:
Elisabetta Farina, Fondazione Don Carlo Gnocchi Onlus (IRCCS), ItalyMarta Altieri, Sapienza University of Rome, Italy
Copyright © 2023 Maggio, De Bartolo, Calabrò, Ciancarelli, Cerasa, Tonin, Di Iulio, Paolucci, Antonucci, Morone and Iosa. 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) and the copyright owner(s) 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.
*Correspondence: Rocco Salvatore Calabrò, U2FsYnJvNzdAdGlzY2FsaS5pdA==
†These authors have contributed equally to this work