Evaluation of Processing Speed of Different Cognitive Functions Across the Life Span Using Cognitive Mobile Games

Empowering sickle cell disease care: the rise of TechnoRehabLab in Sub-Saharan Africa for enhanced patient's perspectives

Sickle-cell Disease (SCD) is a major public health problem in Africa, and there are significant obstacles to its comprehensive management, particularly in terms of access to appropriate healthcare. This calls for inventive approaches to improve patients' prospects. Among the major challenges to be met are the primary and secondary prevention of certain serious complications associated with the disease, such as neurocognitive, motor and respiratory functional disorders. This perspective argues for the rapid creation of specific, cost-effective, technology-supported rehabilitation centres to advance SCD care, identify patients at high risk of stroke and implement tailored rehabilitation strategies. The TechnoRehabLab in Lubumbashi illustrates this shift in thinking by using cutting-edge technologies such as virtual reality (VR), serious games and mobile health to create a comprehensive and easily accessible rehabilitation framework. Diagnostic tools used to perform functional assessment can be used to identify cognitive, balance and walking deficits respectively. Transcranial Doppler enables early detection of sickle cell cerebral vasculopathy, making it possible to provide early and appropriate treatment. VR technology and serious games enable effective rehabilitation and cognitive stimulation, which is particularly advantageous for remote or community-based rehabilitation. In the context of African countries where there is a glaring disparity in access to digital resources, the TechnoRehabLab serves as a tangible example, demonstrating the flexibility and accessibility of technology-assisted rehabilitation. This perspective is an urgent call to governments, non-governmental organisations and the international community to allocate resources to the replication and expansion of similar facilities across Africa.

Machine Learning and Digital Biomarkers Can Detect Early Stages of Neurodegenerative Diseases

Neurodegenerative diseases (NDs) such as Alzheimer's Disease (AD) and Parkinson's Disease (PD) are devastating conditions that can develop without noticeable symptoms, causing irreversible damage to neurons before any signs become clinically evident. NDs are a major cause of disability and mortality worldwide. Currently, there are no cures or treatments to halt their progression. Therefore, the development of early detection methods is urgently needed to delay neuronal loss as soon as possible. Despite advancements in Medtech, the early diagnosis of NDs remains a challenge at the intersection of medical, IT, and regulatory fields. Thus, this review explores "digital biomarkers" (tools designed for remote neurocognitive data collection and AI analysis) as a potential solution. The review summarizes that recent studies combining AI with digital biomarkers suggest the possibility of identifying pre-symptomatic indicators of NDs. For instance, research utilizing convolutional neural networks for eye tracking has achieved significant diagnostic accuracies. ROC-AUC scores reached up to 0.88, indicating high model performance in differentiating between PD patients and healthy controls. Similarly, advancements in facial expression analysis through tools have demonstrated significant potential in detecting emotional changes in ND patients, with some models reaching an accuracy of 0.89 and a precision of 0.85. This review follows a structured approach to article selection, starting with a comprehensive database search and culminating in a rigorous quality assessment and meaning for NDs of the different methods. The process is visualized in 10 tables with 54 parameters describing different approaches and their consequences for understanding various mechanisms in ND changes. However, these methods also face challenges related to data accuracy and privacy concerns. To address these issues, this review proposes strategies that emphasize the need for rigorous validation and rapid integration into clinical practice. Such integration could transform ND diagnostics, making early detection tools more cost-effective and globally accessible. In conclusion, this review underscores the urgent need to incorporate validated digital health tools into mainstream medical practice. This integration could indicate a new era in the early diagnosis of neurodegenerative diseases, potentially altering the trajectory of these conditions for millions worldwide. Thus, by highlighting specific and statistically significant findings, this review demonstrates the current progress in this field and the potential impact of these advancements on the global management of NDs.

Mobile health solutions: An opportunity for rehabilitation in low- and middle income countries?

Mobile health (mHealth) development has advanced rapidly, indicating promise as an effective patient intervention. mHealth has many potential benefits that could help the treatment of patients, and the development of rehabilitation in low- and middle-income countries (LMICs). mHealth is a low-cost option that does not need rapid access to healthcare clinics or employees. It increases the feasibility and rationality of clinical treatment expectations in comparison to the conventional clinical model of management by promoting patient adherence to the treatment plan. mHealth can also serve as a basis for formulating treatment plans and partially compensate for the shortcomings of the traditional model. In addition, mHealth can help achieve universal rehabilitation service coverage by overcoming geographical barriers, thereby increasing the number of ways patients can benefit from the rehabilitation service, and by providing rehabilitation to individuals in remote areas and communities with insufficient healthcare services. However, despite these positive potential aspects, there is currently only a very limited number of studies performed in LMICs using mHealth. In this study, we first reviewed the current evidence supporting the use of mHealth in rehabilitation to identify the countries where studies have been carried out. Then, we identify the current limitations of the implementation of such mHealth solutions and propose a 10-point action plan, focusing on the macro (e.g., policymakers), meso (e.g., technology and healthcare institutions), and micro (e.g., patients and relatives) levels to ease the use, validation, and implementation in LMICs and thus participate in the development and recognition of public health and rehabilitation in these countries.

Development and Evaluation of an Artificial Intelligence–Based Cognitive Exercise Game: A Pilot Study

Recently, cognitive serious games have successfully been employed to train cognitive abilities in elderly people with mild cognitive impairment, Alzheimer’s disease, and related disorders. However, despite the continuous rehabilitation game design and its applications, the existing cognitive exercise games fall short of user interaction and personalized elements with regard to difficult levels, which leads to users leaving early and losing interests during the gameplay. In this regard, the purpose of the study was to design and develop the serious game inclusive of playful elements for user motivation, the web-based mobile application system for easy accessibility, and Artificial Intelligence– (AI–) based difficulty level adjustment system for prevention from earlier leaving out in the middle of the play so that the elderly users can feel entertaining and immersed into the cognitive game voluntarily. This study was designed as an eight-week pilot experiment with thirty-seven participants in their 60s to 80s for the game’s usability assessment purpose. Results of the study showed that the AI-based cognitive exercise game was acceptable, interesting, and motivating for the elderly people and the test results before and after the eight-week training suggest a relationship between longer the training on the game and lower cognitive assessment scores including geriatric quality of life scale, geriatric depression scale, and Korean version of mini-mental state examination (MMSE). These correlations demonstrate the potential value of serious games in clinical assessment of cognitive status for the elderly users with varying cognitive ability. Based on these results, the elderly-centered serious game with playful element can be potentially used in clinical settings, allowing the cognitive training to be more enjoyable and more medically effective. Given these promising results, a more focused study can extend to the game system or additional game tools or features to be explored that solely target the elderly by applying AI and advanced visualization devices.

The Impact of COVID-19 Infection on Cognitive Function and the Implication for Rehabilitation: A Systematic Review and Meta-Analysis

There is mounting evidence that patients with severe COVID-19 disease may have symptoms that continue beyond the acute phase, extending into the early chronic phase. This prolonged COVID-19 pathology is often referred to as ‘Long COVID’. Simultaneously, case investigations have shown that COVID-19 individuals might have a variety of neurological problems. The accurate and accessible assessment of cognitive function in patients post-COVID-19 infection is thus of increasingly high importance for both public and individual health. Little is known about the influence of COVID-19 on the general cognitive levels but more importantly, at sub-functions level. Therefore, we first aim to summarize the current level of evidence supporting the negative impact of COVID-19 infection on cognitive functions. Twenty-seven studies were included in the systematic review representing a total of 94,103 participants (90,317 COVID-19 patients and 3786 healthy controls). We then performed a meta-analysis summarizing the results of five studies (959 participants, 513 patients) to quantify the impact of COVID-19 on cognitive functions. The overall effect, expressed in standardized mean differences, is −0.41 [95%CI −0.55; −0.27]. To prevent disability, we finally discuss the different approaches available in rehabilitation to help these patients and avoid long-term complications.