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Review

Examining Technological Applications Used for the Cognitive Assessment and Rehabilitation of Concussed Individuals: A Rapid Review

by
Isabella P. Garito
1,*,
Sahil Patel
2 and
Lora Appel
1,3,4
1
Faculty of Health, School of Health Policy and Management, York University, Toronto, ON M3J 1P3, Canada
2
Faculty of Health, School of Kinesiology & Health Science, York University, Toronto, ON M3J 1P3, Canada
3
OpenLab, KITE, University Health Network, Toronto, ON M6G 1A5, Canada
4
Michael Garron Hospital, Toronto, ON M4C 3E7, Canada
*
Author to whom correspondence should be addressed.
Technologies 2025, 13(9), 418; https://doi.org/10.3390/technologies13090418
Submission received: 20 June 2025 / Revised: 29 August 2025 / Accepted: 10 September 2025 / Published: 16 September 2025
(This article belongs to the Section Assistive Technologies)
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Abstract

The use of technological applications for cognitive assessment and rehabilitation is growing, yet tools specifically targeting cognition in concussed individuals remain underexplored. This rapid review examined technologies used for cognitive assessment and/or rehabilitation following concussion. Specific objectives were to identify (1) cognitive domains targeted, (2) participant populations recruited, (3) quality of assessment or therapeutic impact, and (4) user involvement in application design. A structured search across three databases yielded 16 articles analyzing 21 applications. Four (25%) focused primarily on cognition, while the remainder addressed multiple domains. Most applications assessed cognition, and study populations frequently included athletes and military members/veterans. Only two (12.5%) studies reported user feedback on application design. Findings suggest a need for broader requirements of concussed civilians to improve representativeness, and for future research to prioritize the development of applications targeting cognitive rehabilitation in concussed populations.

1. Introduction

Concussion, a disruption in neurological activity [1], is classified as a mild traumatic brain injury (mTBI) [2,3]. Public awareness has grown in recent decades, driven by high-profile sports injuries and legal action against major athletic organizations [4]. For years, researchers have referred to mTBIs as a ‘silent epidemic’ as many cases go undetected [5,6] yet can produce persistent symptoms that significantly affect quality of life [5]. In Canada alone, more than 573,000 people aged twelve and older reported sustaining a concussion in the previous 12 months, according to a 2022 Statistic Canada report [7].
The term “mild” in mTBI can be misleading, as it minimizes the potential severity and implies guaranteed recovery [8,9]. In reality, functional brain changes are observed throughout recovery [9], with possible long-term consequences including epilepsy [3], depression, and anxiety, etc. [3,10]. Cognitive impacts, such as deficits in attention, memory, language, and learning, can occur even after a single mTBI [9], with symptoms persisting for months [11] or even years [12]. Cognitive assessments are critical for detecting impairments, while rehabilitation supports recovery of affected functions [13]. Emerging evidence shows that technology-based tools for cognitive assessment and rehabilitation in TBI populations can produce positive outcomes [14,15]. Accordingly, this review examines the current state of research on these technologies, highlighting their advancements, limitations, and design considerations.

2. Background

2.1. Concussion

The terms “concussion”, “mTBI” [10,16,17,18], “mild TBI” [19] and “sport-related concussion (SRC)” [17] are often used interchangeably, creating confusion due to a lack of a clear, concise definition [17,18,20]. For consistency and clarity, this review uses the term “concussion” to also refer to a mTBI [1,21].
Given that loss of consciousness is uncommon [1,3], concussions are often difficult to recognize. Individuals may be asymptomatic [22], misdiagnosed [5], or lack access to health care services [4]; in other cases, symptoms may simply go unrecognized [1,5]. Underreporting is also influenced by context: military service members may downplay symptoms due to a cultural endurance mentality and the absence of physical injuries [5], while athletes may avoid disclosure to prevent removal from play [23]. These factors suggest concussion incidence is higher than what is reflected in official statistics [19].
Diagnosis remains challenging. Conventional imaging techniques such as computed tomography (CT) scans are often ineffective in detecting concussion-related injuries [20,22,24], though other imaging modalities show greater promise [22]. Without proper diagnosis and treatment, symptoms may progress to long-term disability or even death [3] underscoring the serious health risks concussions pose. As a result, ongoing research is exploring more accurate diagnostic strategies, including the development of biomarker databases to predict long-term symptom persistence [25].

2.2. Cognitive Assessment

To fully understand the cognitive effects of a concussion, an assessment of cognitive functions should be conducted prior to initiating rehabilitation [13]. Validated tools can detect deficits [24] and are commonly used to monitor recovery over time [13]. This is especially important for athletes, as returning to sport before full recovery is at risk of second-impact syndrome, a rare but serious condition [3,23]. However, current assessment tools raise concerns about accuracy and bias [26,27]. Concussed individuals may struggle with tasks such as recalling questions, which may unintentionally influence results [26]. One of the most widely used screening tools, Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT), has faced criticism regarding its reliability and limited ability to detect invalid baseline scores, reducing effectiveness for post-injury comparison [27,28]. These limitations highlight the need to investigate technological applications capable of accurate, reliable, and unbiased assessments of cognitive function [26].

2.3. Cognitive Rehabilitation

Cognitive rehabilitation supports recovery from concussion by targeting brain functions affected by the injury [1]. For SRCs, such interventions are offered after four weeks of persistent functional impairment, while in other populations they are initiated after three months [23]. However, many concussed patients do not receive follow up appointments to monitor recovery [29], which restricts access to rehabilitation services. Two main approaches are used in cognitive rehabilitation: compensatory strategies, which help individuals adapt to deficits [30,31] using tools such as notebooks or reminder lists [32], and restorative strategies, which aim to improve impaired functions [30] through exercises like rhyming or using imagery for memory recall [32]. Pharmacological approaches, such as Cerebrolysin, have also shown promise in improving cognitive outcomes [33,34]. Overall, rehabilitation appears to be effective in alleviating concussion symptoms though more rigorous trials are needed to confirm effectiveness [35].

2.4. Use of Technology in Assessment and Rehabilitation

Interest in technology as a tool for assessing and rehabilitating concussion-related deficits has grown significantly [15]. These approaches offer unique advantages, including the ability to assess multiple cognitive domains simultaneously, simulate real-world environments [36], and provide controlled testing conditions [14]. As digital health tools advance, examining their role in concussion-care is critical to understanding their potential to transform cognitive assessment and treatment. This review focuses on interactive digital systems, including Virtual Reality (VR), computer simulations, video games, and robotic devices, applied to the cognitive assessment and/or rehabilitation of concussed individuals.
Emerging technologies show particular promise. For example, eye-tracking can detect impairments in attention, decision making, and overall cognition, etc. [37], that may go unnoticed in standard assessments [38]. As it relies solely on eye movements, eye-tracking is also accessible to individuals with motor impairments, highlighting its accuracy and inclusivity as a tool [38]. Similarly, VR applications, often delivered via a Head Mounted Display (HMD), immerse users in realistic environments by integrating audio and visual stimuli [36,39,40]. This immersive property minimizes external distractions and standardizes testing conditions, enhancing both accuracy and ecological validity [36,39,40]. Collectively these technologies demonstrate significant potential for advancing concussion care and warrant further exploration.
Despite growing interest, few reviews have specifically addressed technology use for cognitive assessment and rehabilitation in the TBI population. Existing reviews have either examined technology in moderate to severe TBI studies [14], focused on narrow domains such as computer-based interventions [41] or eye-tracking [38], or analyzed broader concussion-related deficits across vision, balance, and neurocognition [15]. To our knowledge, no review has comprehensively examined interactive digital systems for the assessment and rehabilitation of cognition in concussed individuals. Given the pace of technological innovation, a rapid review is needed to describe the current literature and map the state of applications in this emerging field.

2.5. Objectives

This rapid review aimed to address the overarching research question: What technologies are currently being studied to facilitate cognitive assessment and rehabilitation in individuals who have sustained a concussion? Specifically, the review sought to: (1) identify the cognitive functions targeted (e.g., memory, attention), (2) evaluate the therapeutic impact or quality of assessment, (3) describe the populations studied (e.g., athletes, general population, etc.) and (4) examine whether end-users were involved in the design of the intervention.

3. Methods

Although not a formal systematic review, the study followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. The review was registered with PROSPERO under the registration number CRD42024545665.

3.1. Search Strategy

On 15 May 2024, searches were conducted in databases relevant to health care research: Ovid MEDLINE, Ovid MEDLINE Epub Ahead of Print and In-Process and Other Non-Indexed Citations, and CINAHL with Full Text (EBSCO). Drawing from reviews of technology use in other populations, key terms were curated and combined into the following search-string strategy: (“Cognit*” AND (“rehabilitation” OR “training” OR “assessment” OR “screening tool”)) AND “mild traumatic brain injury” OR “mild TBI” OR “Concussion” OR “sport related concussion” AND “virtual reality” OR “computer simulation” OR “video game” OR “robots” OR “digital technology”. The full search strategy is provided in Supplementary Material—S1: Search Strategy.
Articles were eligible if they were primary research studies of any design (e.g., randomized control trial, qualitative), with no restriction on year of publication. Studies were included if they described technologies in which users interacted with an application during cognitive assessment or treatment. Exclusion criteria included: (1) studies describing non-interactive technologies (e.g., imaging scans), (2) non-peer-reviewed publications, and (3) the inability to access the full-text. The Population, Intervention, Comparator, and Outcome (PICO) framework [42] was applied to formulate the inclusion/exclusion criteria as noted in Table 1:

3.2. Article Selection

Articles retrieved from the database searches underwent a multi-stage screening process. First, duplicates were removed. Second, titles and abstract were screened independently by two reviewers (IPG and SP) using the inclusion/exclusion criteria described above. Studies deemed ineligible were excluded at this stage. Finally, full text screening was conducted independently by the same two reviewers (IPG and SP). In cases of uncertainty regarding study eligibility, a third reviewer (LA) assessed the article and consensus was reached through discussion.

3.3. Data Extraction/Analysis

All included articles were charted and analyzed in detail. Data were extracted into the following categories: (1) publication/study information (year of publication, study setting, main objectives, overall results, etc.), (2) technological application (cognitive domain(s) targeted, type of tool (assessment and/or rehabilitation), and application name/type), (3) study population (sample size, participant demographics, and population type (e.g., athlete, military/veteran, general population)), and (4) End-user involvement, whether and how participants were engaged in the study design or application development.
Critical appraisal of methodological quality was conducted using the Mixed Methods Appraisal Tool (MMAT) 2018 version [43]. The MMAT was selected for its ability to assess the quality of diverse primary research designs through a structured set of criteria [43,44]. Following data extraction and MMAT screening, information was organized in Google Sheets and imported into Open Refine, which facilitated detailed analysis and data organization [45]. Results were then synthesized into themes, which are presented in the following Results section.

4. Results

Database searches conducted on 15 May 2024 identified 52 articles. After removing 25 duplicates and excluding six studies during title and abstract screening, 21 full-text articles were reviewed. Of these, 16 met the inclusion criteria and were included in the review (Figure 1—Prisma Diagram). A summary of included articles is provided in Supplementary Material—S2: Article Summary.
Of the 16 included studies, nine (56.3%) were conducted in the United States, six (37.5%) in Canada, and one (6.3%) in Australia. Most were published between 2010 and 2019 (10/16, 62.5%), with the remainder published from 2020 onward (6/16, 37.5%); no articles were published after 2023. Study designs varied, and five (31.3%) did not specify a design but were classified using MMAT criteria. Overall, 15 studies (93.8%) met MMAT quality thresholds. One study (6.3%) was excluded due to incomplete data, as only three of the 55 participants completed all intervention sessions because of scheduling conflicts.

4.1. Cognitive Area Studied

Studies were included if cognition was assessed or rehabilitated, regardless of whether it was the primary aim. Four articles (25%) focused exclusively on cognition, five (31.3%) primarily examined other domains (e.g., walking/gait, etc.) and seven (43.8%) addressed cognition and alongside another area (e.g., vision).
Across all 16 studies, 21 cognitive applications were identified and grouped into three categories: (1) cognitive assessment, (2) cognitive rehabilitation, and (3) cognitive assessment and rehabilitation. A cognitive assessment application evaluated the level of cognitive function to determine if there are any impairments present, while a cognitive rehabilitation application aimed to improve cognitive impairments, often by addressing how information is obtained and understood [13]. An application was considered as providing cognitive assessment and rehabilitation, if the application included both functions of the definitions previously mentioned, providing rehabilitation therapy while also consisting of metrics to assess and evaluate the user’s performance. Most applications (18/21, 85.7%) were used solely for assessment, while three (14.3%) combined assessment with rehabilitation. None were designed exclusively for cognitive rehabilitation.
Cognitive domains were categorized based on Harvey’s framework [46], which distinguishes eight domains of cognitive functioning. These domains differ from other published work that typically tend to group cognitive domains together [46]. The most commonly targeted domains were motor skills/construction (16/21, 76.2%), attention and concentration (13/21, 61.9%), and executive function (10/21, 47.6%). Memory (9/21, 42.9%), processing speed (4/21, 19%), or sensation and perception (1/21, 4.8%) were addressed less frequently. Two applications (9.5%) did not specify domains; in one (4.8%), tasks were described but insufficient detail prevented clear classification (Figure 2).

4.2. Technological Cognitive Applications for Concussions

A range of technological applications were used to assess and/or rehabilitate cognition. Studies in which application targeted only non-cognitive functional deficits were beyond the scope of this review and excluded. As many articles described their application as a form of VR, we further categorized technologies by level of immersiveness (i.e., immersive, semi-immersive, non-immersive). Immersive VR applications involve a virtual environment which fully surrounds the user through the use of a HMD [47,48]. Virtual environments projected onto screens surrounding the user are categorized as semi-immersive VR [48], while non-immersive VR involves the use of a computer to interact with an on-screen avatar or environment [47,48]. Computer-based applications simply displayed questionnaires digitally, did not use a 3D interface and/or were not described as a VR application by the article author. A comparison of the number of applications per category is displayed in Figure 3. All applications (100%) incorporated visual stimuli and seven (33.3%) also reported using auditory stimuli, although additional studies may have included audio without explicit mention. Five applications (23.8%) captured body or head movements through motion-tracking systems, including one (4.8%) that used reflective markers. Three applications (14.3%) integrated eye-tracking capabilities. A detailed summary of the application characteristics and article findings is presented in Table 2.

4.3. Participant Populations

Studies recruited participants from a range of populations, including athletes (8/16, 50%), military members and veterans (5/16, 31.3%), the general population (e.g., healthy adults, students, etc.) (5/16, 31.3%), and treatment providers (1/16, 6.3%) (Table 3). For consistency, this review defined individuals 18 years and older as adults, following Canadian and Australian legal standards. Notably, in the United States, adulthood is not uniformly defined until age 21. Among athlete-focused studies, half (4/16, 25%) recruited participants under the age of 17, while the other half (4/16, 25%) enrolled adults 18 years and older. Within the general population category, only one study (6.3%) included participants younger than 17, while the remaining four (25%) recruited adults 18 years and older.
More than half of the studies (9/16, 56.3%) enrolled fewer than 50 participants, the details of which can be found in Table 3. Four studies reported larger sample sizes exceeding 100 participants: two (12.5%) included 100–200 individuals, one (6.3%) enrolled 301–400, and one (6.3%) recruited more than 400 participants. Notably, Merchant-Borna et al. [54] collected baseline data from 403 student athletes but conducted their intervention with only 19 who had sustained a concussion. Levy et al. [50] did not report a total sample size but noted that 6–10 individuals participated per focus group; with fewer than 10 participants in each session.
Eight studies (50%) involved two cohorts of participants. Of these, seven (43.8%) compared concussed and healthy individuals, while one (6.3%) stratified participants by age. Overall, the majority of studies (14/16, 87.5%) enrolled individuals with a history of concussion or a recent concussion. One study (6.3%) recruited participants without a history of head injury, and one (6.3%) did not specify concussion history (Table 3).

4.4. Participant Involvement in Application Design

To evaluate whether end-users were engaged in application development, we assessed whether participants were asked to critique the design and suggest improvements. Only two studies (12.5%) incorporated participant feedback. Levy et al. [50] asked stakeholders (i.e., therapists, veterans with and without a history of mTBI) to describe challenges experienced while interacting with the application and suggestions for improvement. While Horan et al. [36] did not directly collect feedback within the study examined in this review, they describe insights gained from an earlier pilot test involving jockey athletes and coaches. These stakeholders critiqued the application’s resemblance to a jockey match, leading to refinements in the design of the virtual horses [36].

5. Discussion

The goal of this review was to examine the technologies developed for the assessment and rehabilitation of cognition in individuals with concussions. Specifically, we aimed to: (1) identify the cognitive domains targeted by these technologies, (2) evaluate the quality and/or therapeutic impact of the applications, (3) describe participant characteristics to identify patterns across studies and (4) report on the extent of user-centered design.

5.1. Article Information

Although only one article failed to meet the MMAT criteria, many lacked critical reporting details. Information such as study design, participant demographics, intervention duration, and study setting (e.g., university, hospital) was often absent or insufficiently described. Transparent reporting of these elements is essential for accurate interpretation, replication, and synthesis across studies.

5.2. Domains of Cognitive Function Studied

Concussions can affect multiple domains of cognition [9], yet only four studies focused exclusively on assessment or rehabilitation. Notably, no applications addressed language and verbal skills [46], reflecting a broader gap that reflects the underrepresentation of speech-related deficits in concussion research [62].
Most studies assessed multiple domains, with attention and executive function most frequently targeted. VR applications appear especially sensitive to concussion-related impairments, likely due to their ability to simulate real-world contexts requiring attention, and inhibition abilities [39]. Comparisons across modalities (e.g., VR, robotics, computer-based applications), as conducted by Horan et al. [36] and Nolin et al. [39], demonstrated that different technologies can target similar domains, offering clinicians versatile tools. Multi-domain assessments, for example Shepherd et al. [57] that targeted memory, attention, motor skills, and executive function, and particularly those integrating features like eye-tracking, such as Kullmann et al. [37], allow clinicians to evaluate multiple functional capabilities simultaneously, which may provide more efficient and precise assessments while supporting timely clinical decision-making.

5.3. Role of Technological Applications

Many computer-based assessments relied on self-report, a limitation given that individuals may underrecognize or underreport their symptoms [23,61], potentially preventing them from making a full recovery [61]. VR and robotic applications reduced this reliance on self-report by embedding assessments into interactive environments [36,50,51,52], thereby mitigating instances where patients may compensate for impairments. However, learning effects remain a concern [51], particularly when the same platform is used for both testing and training. To address this, outcomes should be validated using independent measures. Additional benefits of VR applications include their ability to obscure a user’s physical environment [39], provide audio and visual instructions [63,64], and simulate scenarios experienced in daily living [40]. Most applications reviewed were assessment-only (19/21, 85.7%), with few combining assessment with rehabilitation (3/21, 14.3%), and none rehabilitation-only. This imbalance likely reflects the greater methodological demands of rehabilitation trials, which require longer follow-up and robust clinical endpoints [65]. While hybrid tools can still be valuable, rehabilitation-specific applications remain underdeveloped.
Ultimately, the benefits described in this section provide evidence that technological applications are able to uphold the reliability, accuracy, and validity needed to perform a cognitive assessment and/or rehabilitation, however ethical considerations also warrant attention. Several applications collected sensitive data (e.g., eye-tracking), underscoring the need for transparent consenting procedures, clear data storage location (e.g., on device or in cloud) and protocols, as well as compliance with privacy regulations including access by third party sponsors [66]. These considerations are especially important when adolescents are involved due to consent requirements and increased data-protection needs. Finally, while Artificial Intelligence (AI) was not specifically analyzed in this review, we acknowledge the role AI has in our current and future health care system, its ability to provide improved access to care, and the additional challenges it may present related to privacy and confidentiality [67].

5.4. Participant Sample

Athletes and military personnel were disproportionately represented, comprising 56.3% of included studies. While many articles did not provide a justification for recruiting athletes, this disproportionate focus is not surprising given that cognitive therapy in the general population remains understudied [30]. Although athletes and other high-risk groups often receive faster access to cognitive rehabilitation [23], the limited representation of civilian populations raises important concerns about the broader generalizability of these applications.
Concussed patients are the primary beneficiaries of technological cognitive assessments and rehabilitation and should remain central to the research. However, other stakeholders such as clinicians and caregivers, were rarely included; in fact, only one study (Levy et al. [50]) (6.3%) in this review recruited therapists. Broader engagement is both feasible and valuable: Hunt et al. [68], for example, found that concussion stakeholders including patients, family members, health professionals, and community care workers expressed willingness to participate in research to ensure accurate reporting of symptoms and alignment with patient needs. We strongly recommend that future studies incorporate a wider range of stakeholders to support the design of interventions that are both relevant and implementable. Additionally, five studies in this review reported small sample sizes [53,58,59,60,61] which may have limited the reliability of their findings. To strengthen the evidence base, future research should prioritize larger, more representative samples drawn from both specialized and general populations.

5.5. User-Centered Design

Despite increasing emphasis on stakeholder involvement [69], particularly patient engagement as a strategy to improve acceptability, only two studies, Horan et al. [36] and Levy et al. [50], incorporated participant feedback. End-user engagement is critical for ensuring that technological interventions are both effective and acceptable [70]. While van der Ham et al. [71] reported that healthcare professionals generally view cognitive rehabilitation technologies as beneficial and easy to use, hesitancy can arise when external pressures from stakeholders drive adoption. Such dynamics may hinder acceptance and limit successful implementation. Or et al. [70] outlines three key aspects of post-development user testing: (1) examining the application’s capabilities, appropriateness for the target population and setting; (2) evaluating potential acceptance by end-users; and (3) studying its ability to achieve its intended purpose. Although these guidelines were developed for mobile health applications, they are equally relevant to the technologies reviewed here, which similarly require end-user acceptance for successful implementation. User-centered design was effectively demonstrated in Levy et al. [50], where focus groups identified concerns about user interaction that informed improvements in the V-Mart controller; subsequent focus groups expressed greater enthusiasm about the application’s role in cognitive and emotional assessments and rehabilitation. Likewise, Horan et al. [36] reported revisions to visual design following user testing. Both studies highlight the value of end-user feedback in enhancing application usability. For many other applications in this review, particularly those developed by organizations not associated with the authors of the article, collecting and reporting user feedback would have provided stronger insights into acceptability, aligning with Or et al.’s [70] second guideline and offering a more meaningful evaluation of end-user engagement.

5.6. Data Comparison Between Reviews

Reneker et al. [15] conducted a scoping review examining the use of technology in concussion assessment and rehabilitation across multiple functions, including neurocognition. Several differences emerged when compared to the present review. Unlike Reneker et al. [15], this review did not identify any mobile applications but did report the use of robotic applications, which were absent in the earlier work. No articles categorized as neurocognition in Reneker et al. [15] overlapped with the studies included here; however, one study by Rábago and Wilken [55] was classified by Reneker et al. [15] as balance and dual-task but was included in this review because it assessed and rehabilitated executive function, reaction time, and processing speed. This review also identified a greater number of studies focused specifically on cognition, thereby adding novel data to the existing literature. In addition, unlike Reneker et al. [15], this review examined participant populations and the involvement of end-users in application design, two areas that provide important insights for implementation and future research.

6. Limitations

This review has several limitations. First, all included studies were conducted in only three countries, which may introduce regional bias and limit generalizability. This constraint was partly caused by the requirement to include only English-language publications, which likely excluded studies from non-English-speaking regions. While this narrow geographic scope limits global representation, the concentration of research in Canada and the United States (15/16 studies) also allows for more detailed region-specific analysis and policy relevance.
Second, the search strategy was restricted to a select number of health-related databases due to time constraints, which may have limited the comprehensiveness of the review. Third, Harvey’s [46] eight-domain framework was used to categorize cognitive outcomes. While this provided a structured lens, other taxonomies group domains differently, which may influence interpretation and cross-study comparisons. Finally, terminology inconsistencies used across the studies present challenges. The terms “mild traumatic brain injury” (mTBI) and “concussion” were often used interchangeably, and in some cases Authors used “mTBI” without explicitly confirming a concussion diagnosis [50,56,61]. This lack of clarity introduces uncertainty regarding participant populations and may affect the specificity of the findings to concussions.

7. Conclusions

Concussion care, from diagnosis to full recovery, remains a complex process and is hampered by inconsistent terminology around concussion and mTBI [72]. This review identified a diverse set of technological applications targeting cognition, underscoring progress in the field of digital health. However, few studies focused solely on cognition; most addressed multiple functional domains, reflecting an integrative approach that may help clinicians better capture the broad impact of a concussion. Only four studies used applications that combined cognitive assessment with rehabilitation, highlighting a need for development in this area. Expanding research populations beyond athletes and military members is also critical, given the significant burden of concussions in the general population [7]. A key gap is the limited involvement of end-users in application design. Incorporating feedback from clients, patients, and clinicians is essential for improving usability, acceptance, and real-world adoption. Future research should prioritize user-centered design, standardized terminology, and broader recruitment strategies. Additional funding is recommended to accelerate the development and evaluation of cognitive applications for concussion care. Overall, the technologies reviewed show considerable promise, but further innovation, inclusivity, and methodological rigor are needed to fully realize their potential for improving cognitive assessment and rehabilitation in concussion populations.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/technologies13090418/s1, S1: Search Strategy; S2: Article Summary.

Author Contributions

Conceptualization: I.P.G. and L.A.; Methodology: I.P.G.; Formal Analysis: I.P.G. and S.P.; Writing—Original Draft: I.P.G. and S.P.; Writing—Review & Editing: L.A., I.P.G. and S.P.; Supervision: L.A. 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 datasets supporting the conclusions of this article are included within the article and Supplementary Files.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
CTComputed Tomography
DANADefense-Automated Neurobehavioral Assessment
EEGElectroencephalograph
HIEQHighmark Interactive Equilibrium
HMDHead Mounted Display
ImPACTImmediate Post-Concussion Assessment and Cognitive Testing
MMATMixed Methods Appraisal Tool
mTBIMild Traumatic Brain Injury
PICOPopulation, Intervention, Comparator, and Outcome
PRISMAPreferred Reporting Items for Systematic reviews and Meta-Analyses
SRCSports Related Concussion
VIGIL-CPTVigil Continuous Performance Test
VRVirtual Reality
VRaiVirtual Reality avatar interaction

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Technologies 13 00418 g001
Figure 1. Prisma diagram.
Figure 1. Prisma diagram.
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Figure 2. Cognitive domains assessed and/or rehabilitated using technological application.
Figure 2. Cognitive domains assessed and/or rehabilitated using technological application.
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Figure 3. Types of cognitive applications analyzed in articles.
Figure 3. Types of cognitive applications analyzed in articles.
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Table 1. PICO method with inclusion and exclusion criteria.
Table 1. PICO method with inclusion and exclusion criteria.
PICO CategoriesInclusion CriteriaExclusion Criteria
PopulationAddresses the concussion population but can include clinicians, caregivers, or non-concussed population.Studies involving other populations but where the tool is not intended for the concussion population.
InterventionAny technological application that involves the use of interactive digital system applications (e.g., VR, computer simulations, video games, etc.).Technology that does not involve the use of an interactive digital system application.
ComparatorArticles comparing technology and other types of interventions. Articles with no comparator were also included.Articles where the concussion population’s results are not reported.
OutcomeMust involve cognitive assessment and/or rehabilitation as identified by the article author. Articles could also be evaluating other areas of rehabilitation.Articles that do not analyze cognitive assessment and/or rehabilitation.
Table 2. Summary of Articles.
Table 2. Summary of Articles.
Article Author & YearName of Application(s)Type of Application(s)Purpose of Application(s)Aim of StudyKey Findings
Horan et al., 2020 [36](1) Cogstate
(2) CONVIRT		(1) Computer-based
(2) Immersive VR		Cognitive AssessmentExamined the test/retest reliability of CONVIRT, compared the validity of two applications, and assessed if the CONVIRT application evokes a physiological arousal in participants.CONVIRT VR application was found to have a high test–retest performance, acceptable validity, and evokes a higher physiological response.
Kullmann et al., 2021 [37](1) Neurolign Dx NOTC (used in 64.37% of participants)
(2) Neurolign Dx 100 (used in 35.6% of participants)		(1) Semi-immersive VR
(2) Immersive VR		Cognitive AssessmentCreate a normative database containing data on functions beneficial for decision making in the diagnoses to recovery phases of a concussion.Use of applications with eye tracking resulted in precise measurements of function and creation of a database. Data can be compared to that of a post concussed patient.
Lempke et al., 2021 [49]CNS Vital SignsComputer-basedCognitive AssessmentAssessed reaction time during various driving sequences using a driving simulator and analyzed CNS Vital Signs domains against driving reaction time.Deficits were found in the driving performance of the concussed cohort. CNS Vital Signs and driving application indicating cognitive applications should not be used to assess driving capabilities.
Levy et al., 2019 [50]V-MartNon-immersive VRCognitive Assessment and RehabilitationGain participant feedback regarding the acceptance, applicability, and usefulness of V-Mart.Participants made recommendations on how the application could be improved and were interested in using the application for assessment and rehabilitation. V-Mart received a high usability scoring.
Little et al., 2015 [51]KINARMRobotics with semi-immersive VRCognitive AssessmentAssess the reliability of KINARM application in healthy participants.Application was found to have high reliability but, a learning effect was found during the first and second sessions impacting the results of the intra-class correlation coefficients.
Little et al., 2016 [52]KINARMRobotics with semi-immersive VRCognitive AssessmentAnalyze KINARM results in previously concussed and healthy individuals.No difference in testing results between both cohorts.
McFarlane et al., 2020 [53]Spatial Configuration TaskNon-immersive VRCognitive AssessmentEvaluate if participants with an SRC can create a cognitive map for the purpose of spatial configuration.Participants who had sustained a concussion received low test results compared to their counterparts.
Merchant-Borna et al., 2017 [54]ImPACTComputer-basedCognitive AssessmentAssess the feasibility of a balance application on a user’s stability and compare it to ImPACT, and a second balance application to determine the validity of the applications when evaluating recovery post-concussion.The technological balance application is able to identify additional information not available in the results of the ImPACT test. There were two occasions where a participant was considered recovered by ImPACT and the second balance application but listed as not recovered by the technological balance application.
Nolin et al., 2012 [39](1) Vigil Continuous Performance Test (VIGIL-CPT)
(2) ClinicaVR: Classroom-CPT		(1) Computer-based
(2) Immersive VR		Cognitive AssessmentEvaluate performance between two applications in participants with and without a history of concussions.The ClinicaVR: Classroom-CPT was found to detect increased deficits in attention and inhibition, not detected by the VIGIL-CPT.
Rábago & Wilken, 2011 [55]Computer Assisted Rehabilitation Environment (CAREN)Semi-immersive VRCognitive Assessment and RehabilitationDescribes the use of CAREN on a patient with several post-concussion symptoms.Following multiple sessions, the patient’s symptoms had resolved with the exception of a headache that continued to occur.
Robitaille et al., 2017 [56]Virtual Reality avatar interaction (VRai)Immersive VRCognitive AssessmentAims to determine if using a dual-task walking procedure could detect deficits in executive dysfunction. Also assessed interactions within the virtual environment to analyze user acceptance, and level of immersiveness.Few differences were noted between both cohorts, but authors observed a more cautious interaction of healthy participants with the avatars present in the VR application compared to the mTBI cohort.
Shepherd et al., 2022 [57]Highmark Interactive Equilibrium (HIEQ)Computer-basedCognitive AssessmentAnalyze the feasibility and reliability of the application.While considered feasible, HIEQ was found to have low to medium reliability when individuals were completing the assessment in the same room.
Teel et al., 2014 [58](1) ImPACT
(2) Application provided by HeadRehab		(1) Computer-based
(2) Semi-immersive VR		Cognitive AssessmentEvaluate neural areas of the brain impacting performance and use an electroencephalograph (EEG) to assess participants at baseline, and while completing the ImPACT and HeadRehab applications.No statistically significant difference in ImPACT scoring and only a small difference was observed for the semi-immersive VR application. However, EEG results indicate differences between the concussed and healthy participants at baseline and during application testing which was not detected by ImPACT or the HeadRehab application.
Teel et al., 2016 [59](1) ImPACT
(2) Application provided by HeadRehab		(1) Computer-based
(2) Semi-immersive VR		Cognitive AssessmentAims to measure sensitivity and specificity of HeadRehab application.Did not report on the results of ImPACT but noted that the semi-immersive VR application accurately assessed cognition between participants with and without a concussion.
Wilkerson et al., 2021 [60]TRAZER® Sports SimulatorNon-immersive VRCognitive Assessment and RehabilitationDetermine the reliability, discriminatory power, variation in performance and accuracy of application to identify perceptual-motor functions to support management of recovery post SRC.The simulator was able to identify differences in scoring between participants with a history of SRC and those without a prior SRC. Application was found to have high reliability.
Wright et al., 2018 [61]Defense-Automated Neurobehavioral Assessment (DANA)Computer-basedCognitive AssessmentAuthors hypothesized that deficits in cognition, postural control, and sensory reactivity would be present in participants who had sustained a past mTBI compared to their healthy counterparts.Sustaining a past mTBI did not result in any statistically significant findings in cognition. Impairments were found in balance and sensory reactivity.
Table 3. Participant demographics.
Table 3. Participant demographics.
Article Author
and Year		Sample SizeAges of ParticipantsParticipant PopulationParticipants with a Concussion or a History of Concussion(s)
Horan et al., 2020 [36]16518–34 General populationConcussed participants not recruited. Information on history of concussion not available.
Kullmann et al., 2021 [37]46618–45General population, military members, athletesNot recruited.
Lempke et al., 2021 [49]28Concussed cohort: 20.2 ± 0.9 years*
Control cohort:
20.4 ± 1.1 years*		General population14 participants had an asymptomatic concussion.
Levy et al., 2019 [50]Not reported.Inclusion criteria indicates participants were 18 years of age and older*Therapists, military veterans3 participants with mTBI in initial focus group, 4 participants with mTBI in follow-up focus group.
Little et al., 2015 [51]3410–14Athletes9 participants had a history of concussion.
Little et al., 2016 [52]38510–14Athletes94 participants had a history of concussion.
McFarlane et al., 2020 [53]3711–16Athlete18 participants with a concussion.
Merchant-Borna et al., 2017 [54]403 individuals followed.
19 enrolled in study following concussion.		Mean age of 19.2 ± 1.2 years*Athletes19 participants had a concussion, 6 participants also had a history of concussion.
Nolin et al., 2012 [39]50Mean age of 13.64 years*Athletes25 participants had experienced a concussion 1–24 months before study.
Rábago & Wilken, 2011 [55]131Military member1
Robitaille et al., 2017 [56]12Concussed cohort: 30.3 ± 8.6 years*
Control cohort:
30.3 ± 5.3 years*		Military members6 participants had a concussion 2 weeks to 7 months before study.
Shepherd et al., 2022 [57]5514–19General population28 participants had a history of a concussion.
4 participants had missing data.
Teel et al., 2014 [58]19–20 (Exact number of participants unclear).Mean age of 21 ± 1*General population7 participants had a concussion.
Teel et al., 2016 [59]152Mean age of 21 ± 2.4*Athletes24 participants had a concussion.
Wilkerson et al., 2021 [60]1619–44Athletes9 participants had a history of concussion.
Wright et al., 2018 [61]36History of mTBI: 33.57 (7.93)*
No history of mTBI: 25.95 (4.48)*		Military members14 participants had a history of mTBI.
* Age range of participants not reported.
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Garito, I.P.; Patel, S.; Appel, L. Examining Technological Applications Used for the Cognitive Assessment and Rehabilitation of Concussed Individuals: A Rapid Review. Technologies 2025, 13, 418. https://doi.org/10.3390/technologies13090418

AMA Style

Garito IP, Patel S, Appel L. Examining Technological Applications Used for the Cognitive Assessment and Rehabilitation of Concussed Individuals: A Rapid Review. Technologies. 2025; 13(9):418. https://doi.org/10.3390/technologies13090418

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Garito, Isabella P., Sahil Patel, and Lora Appel. 2025. "Examining Technological Applications Used for the Cognitive Assessment and Rehabilitation of Concussed Individuals: A Rapid Review" Technologies 13, no. 9: 418. https://doi.org/10.3390/technologies13090418

APA Style

Garito, I. P., Patel, S., & Appel, L. (2025). Examining Technological Applications Used for the Cognitive Assessment and Rehabilitation of Concussed Individuals: A Rapid Review. Technologies, 13(9), 418. https://doi.org/10.3390/technologies13090418

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