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Cognitive impairment is common in persons with spinal cord injury (SCI), impacting their daily functioning and rehabilitation. This study assesses the extent of self-reported cognitive failures in everyday life in persons with SCI and its relationships with objective neurocognitive measures and psychosocial factors, including depressive mood, anxiety, perceived control, and fatigue. The differences between forty-one adults with a chronic SCI and forty-one able-bodied controls were examined. The participants completed the Cognitive Failures Questionnaire (CFQ) to assess cognitive failure and neurocognitive tests assessing attention and executive functions, as well as a psychosocial assessment. The SCI group reported higher cognitive failure rates than the able-bodied group (31.7% versus 19%, p > 0.05). Objective neurocognitive tests did not significantly correlate with the CFQ scores in either group. However, the CFQ scores were positively associated with most psychosocial factors, even after controlling for covariates. The CFQ scores were significantly associated with depressive mood in persons with SCI. These findings highlight the importance of incorporating self-reported cognitive measures into neurocognitive assessments and rehabilitation planning for adults with SCI. Self-reports capture everyday cognitive challenges that objective tests may miss. Additionally, this study highlights the strong connections between cognitive failures and psychosocial issues, particularly mood disorders, emphasizing the need for comprehensive rehabilitation and psychosocial support post-SCI, addressing both cognitive and emotional wellbeing. [ABSTRACT FROM AUTHOR]

Background: Mild cognitive impairment (MCI) is a common secondary condition associated with spinal cord injury (SCI). Cognitive reserve (CR) is believed to protect against cognitive decline and can be assessed by premorbid intelligence (pmIQ). Despite the potential utility of pmIQ as a complementary metric in the evaluation of MCI in SCI, this approach has been infrequently employed. The purpose of this study was to examine the association between MCI and pmIQ in adults with SCI with the aim of exploring the potential value of pmIQ as a marker of CR in this population. Methods: Cognitive function was assessed on three occasions in adults with SCI over a 12-month period post-injury, and pmIQ was assessed once at baseline. Demographic and mental health measures were also collected, and logistic regression was conducted to determine the strength of association between pmIQ and MCI while adjusting for factors such as mental health and age. Results: The regression analysis revealed that at the time of admission to SCI rehabilitation, the MCI assessed by a valid neurocognitive screen was strongly associated with pmIQ. That is, if a person has MCI, there was 5.4 greater odds (p < 0.01) that they will have poor pmIQ compared to a person without MCI after adjustment for age and mental health. Conclusions: The assessment of CR is an important area that should be considered to improve the process of diagnosing MCI in adults with an acute SCI and potentially facilitate earlier intervention to slow or prevent cognitive decline. [ABSTRACT FROM AUTHOR]

Age-related hearing loss affects a significant proportion of adults aged 60 years and above, with a prevalence of 65%. This condition has a negative impact on both physical and mental well-being, and while hearing interventions can help alleviate the effects of hearing loss, they cannot completely restore normal hearing or halt the progression of age-related hearing loss. Oxidative stress and inflammation have been identified as potential contributors to this condition. By addressing modifiable lifestyle risk factors that exacerbate oxidative stress, there may be an opportunity to prevent hearing loss. Therefore, this narrative review provides an overview of the major modifiable lifestyle risk factors associated with age-related hearing loss, that is, exposure to noise and ototoxic chemicals, smoking, diet, physical activity, and the presence of chronic lifestyle diseases, and offers an overview of the role of oxidative stress in the pathophysiology of this condition. [ABSTRACT FROM AUTHOR]

This study examined the brain source space functional connectivity from the electroencephalogram (EEG) activity of 48 participants during a driving simulation experiment where they drove until fatigue developed. Source-space functional connectivity (FC) analysis is a state-of-the-art method for understanding connections between brain regions that may indicate psychological differences. Multi-band FC in the brain source space was constructed using the phased lag index (PLI) method and used as features to train an SVM classification model to classify driver fatigue and alert conditions. With a subset of critical connections in the beta band, a classification accuracy of 93% was achieved. Additionally, the source-space FC feature extractor demonstrated superiority over other methods, such as PSD and sensor-space FC, in classifying fatigue. The results suggested that source-space FC is a discriminative biomarker for detecting driving fatigue. [ABSTRACT FROM AUTHOR]

Adults with spinal cord injury (SCI) are reported to have heightened risk of cognitive impairment, notably mild cognitive impairment (MCI). Reports of increased risk of MCI are almost exclusively based on cross-sectional assessments of cognitive function using norm-referenced scores. Norm-referenced single-point assessments do not reflect cognitive decline at the individual level but rather represent between group differences in cognitive function. The practice of relying solely on norm-referenced assessment to study MCI after SCI is therefore problematic as it lends to potential misclassification of MCI. Premorbid intelligence estimates permit comparison of people's actual versus expected cognitive function and thereby can be used to validate the presence of genuine cognitive decline. These are not utilized in the assessment of MCI after SCI. This study simulated data for 500,000 adults with SCI to compare norm-referenced and premorbid-intelligence methods of screening for MCI to examine the potential extent of MCI misclassification after SCI resulting from the overreliance on norm-referenced methods and exclusion of premorbid intelligence methods. One in five to one in 13 simulated adults with SCI were potentially misclassified as having MCI showing that measures of premorbid cognitive function must be included in assessment of cognitive function after SCI. [ABSTRACT FROM AUTHOR]

Computed Tomography (CT) is commonly used for cancer screening as it utilizes low radiation for the scan. One problem with low-dose scans is the noise artifacts associated with low photon count that can lead to a reduced success rate of cancer detection during radiologist assessment. The noise had to be removed to restore detail clarity. We propose a noise removal method using a new model Convolutional Neural Network (CNN). Even though the network training time is long, the result is better than other CNN models in quality score and visual observation. The proposed CNN model uses a stacked modified U-Net with a specific number of feature maps per layer to improve the image quality, observable on an average PSNR quality score improvement out of 174 images. The next best model has 0.54 points lower in the average score. The score difference is less than 1 point, but the image result is closer to the full-dose scan image. We used separate testing data to clarify that the model can handle different noise densities. Besides comparing the CNN configuration, we discuss the denoising quality of CNN compared to classical denoising in which the noise characteristics affect quality. [ABSTRACT FROM AUTHOR]

Spinal cord injury (SCI) is a severe neurological injury that results in damage to multiple bodily systems. SCI rehabilitation requires a significant focus on improving adjustment to the injury. This paper presents a detailed description of the Spinal Cord Injury Adjustment Model (SCIAM), which clarifies how individuals adjust to SCI and contends that adjustment to SCI is a multifactorial process involving non-linear dynamic adaptation over time. Evidence supporting SCIAM is also discussed. Mediation analyses were conducted to test the mediator dynamics proposed by the model. The analyses tested the relationship between two moderators (self-care and secondary health conditions), mediators (two self-efficacy items and appraisal of quality of life or QoL), and positive versus negative vitality/mental health as outcomes. Results showed that higher self-efficacy and perceived QoL was related to greater independence in self-care and reduced negative impacts of secondary health conditions. This study supported the mediation role of self-efficacy and other appraisals such as perceived QoL in enhancing self-care and buffering the negative impact of health challenges. In conclusion, it is important to employ a holistic model such as SCIAM to conceptualise and increase understanding of the process of adjustment following a severe neurological injury such as SCI. [ABSTRACT FROM AUTHOR]

Dietary flavonoids have antioxidant, anti-inflammatory, and vascular health benefits, which align with the proposed pathophysiology of age-related eye conditions and hearing problems (hearing loss and tinnitus). This scoping review is based on Arksey and O'Malley's six-stage framework and aims to summarise current evidence on the association between the dietary flavonoid intake and chronic sensory conditions in adults, and to identify the research gaps in this area. Eligible studies were identified by searching MEDLINE, EMBASE PsycINFO via the OVID platform, and Google Scholar, as well as manually searching the reference lists of the eligible articles. The inclusion criteria included: articles with full-text access, written in the English language, and focused on chronic sensory conditions and dietary flavonoid intake in an adult population. Studies focused on flavonoid supplements were excluded. Ten studies were included in this review. The evidence suggests that the flavonoid subclass, flavonols, are protective against eye conditions, including age-related macular degeneration, cataract, and glaucoma. There is insufficient evidence to support an association with hearing loss or tinnitus. Overall, dietary flavonol intake appears to be protective against some chronic eye conditions. However, for most eye and hearing-related conditions, only one study was identified. Thus, there is a need for more recent high-quality research to be conducted to confirm any significant associations. [ABSTRACT FROM AUTHOR]

The assessment of mild cognitive impairment (MCI) following spinal cord injury (SCI) is vital. However, there are no neurocognitive screens which have been developed specifically to meet the unique requirements for SCI, nor are there consistent MCI criteria applied to determine the rates of MCI. The aim of this study was to determine the suitability of a neurocognitive screen for assessing MCI in adults with SCI. A total of 127 participants were recruited. Socio-demographic and injury related variables were assessed. All participants completed the screen. Descriptive statistics are provided for total/domain screen scores and all items, and the screen's ability to distinguish MCI was examined. Congeneric confirmatory factor analyses (CFA) were employed to investigate structural validity. The screen total score was sensitive to differences in neurocognitive capacity, as well as for time since the injury occurred (p < 0.01). The MCI rate ranged between 17–36%. CFA revealed attention and visuoconstruction domains had an adequate model fit and executive function had poor fit, while CFA models for memory and language did not fit the data (did not converge), hence could not be determined. While the screen differentiated between those with MCI and those without, and MCI as a function of time since injury, limitations of its suitability for assessing MCI after SCI exist, demonstrating the need for a specialized neurocognitive screen for adults with SCI. [ABSTRACT FROM AUTHOR]

Neuropathic pain in people with spinal cord injury is thought to be due to altered central neuronal activity. A novel therapeutic intervention using virtual reality (VR) head-mounted devices was investigated in this study for pain relief. Given the potential links to neuronal activity, the aim of the current study was to determine whether use of VR was associated with corresponding changes in electroencephalography (EEG) patterns linked to the presence of neuropathic pain. Using a within-subject, randomised cross-over pilot trial, we compared EEG activity for three conditions: no task eyes open state, 2D screen task and 3D VR task. We found an increase in delta activity in frontal regions for 3D VR with a decrease in theta activity. There was also a consistent decrease in relative alpha band (8–12 Hz) and an increase in low gamma (30–45 Hz) power during 2D screen and 3D VR corresponding, with reduced self-reported pain. Using the nonlinear and non-oscillatory method of extracting fractal dimensions, we found increases in brain complexity during 2D screen and 3D VR. We successfully classified the 3D VR condition from 2D screen and eyes opened no task conditions with an overall accuracy of 80.3%. The findings in this study have implications for using VR applications as a therapeutic intervention for neuropathic pain in people with spinal cord injury. [ABSTRACT FROM AUTHOR]

Through their results, the authors of these papers have provided a better understanding of cognitive states and brain activity based on different EEG signal processing methodologies and machine learning strategies. In this Special Issue, many novel EEG signal-processing strategies and analysis techniques are explored. Electroencephalography (EEG) signals are used widely in clinical and research settings. As EEG signals are acquired from the scalp, this paper presented an anatomically realistic textile-based head phantom for the assessment of EEG sensors. [Extracted from the article]

Tinnitus is the phantom perception of sound in the ears or head that increases in prevalence as age increases. With strong evidence supporting the benefits of dietary fibre for vascular health and hearing loss, intake of dietary fibre may also have a role in the prevention of tinnitus symptoms. This longitudinal study aims to determine the association between the intake of dietary fibre and other carbohydrate nutrition variables including glycaemic index (GI), glycaemic load (GL) and total carbohydrate intakes, and incident tinnitus over 10 years. Of the 1730 participants (aged ≥50 years) from the Blue Mountains Hearing Study with complete baseline data on tinnitus symptoms and carbohydrate intakes, 536 (31%) cases of tinnitus were identified and excluded from further incidence analysis. Dietary data were collected using a validated semi-quantitative food frequency questionnaire to determine intakes of total dietary fibre and fibre contributions from cereals, vegetables, and fruit. A purpose-built database based on Australian GI values was used to calculate mean GI. Lower versus higher intakes of fruit fibre (≤3.6 g/day vs. >3.6 g/day) and cereal fibre (≤4.2 g/day vs. >4.2 g/day) were significantly associated with a 65% (HR = 1.65; 95% CI: 1.15–2.36) and 54% (HR = 1.54; 95% CI: 1.07–2.22) increased risk of developing tinnitus over 10 years, respectively. Associations between intake of other carbohydrate nutrients and incident tinnitus were mostly non-significant. In summary, our study showed modest associations between intake of dietary fibre and incident tinnitus. The protective effects of fibre, particularly insoluble fibre, could underlie observed associations by reducing the risk of tinnitus via vascular risk factors such as cardiovascular disease. Further longitudinal studies evaluating different types and sources of fibre and tinnitus risk are needed to confirm our study findings. [ABSTRACT FROM AUTHOR]

Anterior cruciate ligament (ACL) injury represents one of the main disorders affecting players, especially in contact sports. Even though several approaches based on artificial intelligence have been developed to allow the quantification of ACL injury risk, their applicability in training sessions compared with the clinical scale is still an open question. We proposed a machine-learning approach to accomplish this purpose. Thirty-nine female basketball players were enrolled in the study. Leg stability, leg mobility and capability to absorb the load after jump were evaluated through inertial sensors and optoelectronic bars. The risk level of athletes was computed by the Landing Error Score System (LESS). A comparative analysis among nine classifiers was performed by assessing the accuracy, F1-score and goodness. Five out nine examined classifiers reached optimum performance, with the linear support vector machine achieving an accuracy and F1-score of 96 and 95%, respectively. The feature importance was computed, allowing us to promote the ellipse area, parameters related to the load absorption and the leg mobility as the most useful features for the prediction of anterior cruciate ligament injury risk. In addition, the ellipse area showed a strong correlation with the LESS score. The results open the possibility to use such a methodology for predicting ACL injury. [ABSTRACT FROM AUTHOR]

Overuse-related musculoskeletal injuries mostly affect athletes, especially if involved in preseason conditioning, and military populations; they may also occur, however, when pathological or biological conditions render the musculoskeletal system inadequate to cope with a mechanical load, even if moderate. Within the MOVIDA (Motor function and Vitamin D: toolkit for risk Assessment and prediction) Project, funded by the Italian Ministry of Defence, a systematic review of the literature was conducted to support the development of a transportable toolkit (instrumentation, protocols and reference/risk thresholds) to help characterize the risk of overuse-related musculoskeletal injury. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach was used to analyze Review papers indexed in PubMed and published in the period 2010 to 2020. The search focused on stress (overuse) fracture or injuries, and muscle fatigue in the lower limbs in association with functional (biomechanical) or biological biomarkers. A total of 225 Review papers were retrieved: 115 were found eligible for full text analysis and led to another 141 research papers derived from a second-level search. A total of 183 papers were finally chosen for analysis: 74 were classified as introductory to the topics, 109 were analyzed in depth. Qualitative and, wherever possible, quantitative syntheses were carried out with respect to the literature review process and quality, injury epidemiology (type and location of injuries, and investigated populations), risk factors, assessment techniques and assessment protocols. [ABSTRACT FROM AUTHOR]

The latest studies in virtual reality (VR) have evidenced the potential of this technology to reproduce environments from multiple domains in an immersive way. For instance, in stress relief research, VR has been presented as a portable and inexpensive alternative to chromotherapy rooms, which require an adapted space and are expensive. In this work, we propose a portable and versatile alternative to the traditional chromotherapy color-loop treatment through four different 360-degree virtual experiences. A group of 23 healthy participants (mean age 22.65 ± 5.48) were conducted through a single-session experience divided into four phases while their electroencephalography (EEG) was recorded. First, they were stressed via the Montreal imaging stress task (MIST), and then relaxed using our VR proposal. We applied the Wilcoxon test to evaluate the relaxation effect in terms of the EEG relative gamma and self-perceived stress surveys. The results that we obtained validate the effectiveness of our 360-degree proposal to significantly reduce stress (p-value = 0.0001). Furthermore, the participants deemed our proposal comfortable and immersive (score above 3.5 out of 5). These results suggest that 360-degree VR experiences can mitigate stress, reduce costs, and bring stress relief assistance closer to the general public, like in workplaces or homes. [ABSTRACT FROM AUTHOR]

Motor imagery (MI) based brain–computer interface (BCI) aims to provide a means of communication through the utilization of neural activity generated due to kinesthetic imagination of limbs. Every year, a significant number of publications that are related to new improvements, challenges, and breakthrough in MI-BCI are made. This paper provides a comprehensive review of the electroencephalogram (EEG) based MI-BCI system. It describes the current state of the art in different stages of the MI-BCI (data acquisition, MI training, preprocessing, feature extraction, channel and feature selection, and classification) pipeline. Although MI-BCI research has been going for many years, this technology is mostly confined to controlled lab environments. We discuss recent developments and critical algorithmic issues in MI-based BCI for commercial deployment. [ABSTRACT FROM AUTHOR]

The large and growing burden of chronic heart failure (CHF) on healthcare systems and economies is mainly caused by a high hospital admission rate for acute decompensated heart failure (HF). Several remote monitoring techniques have been developed for early detection of worsening disease, potentially limiting the number of hospitalizations. Over the last years, the scope has been shifting towards the relatively novel invasive sensors capable of measuring intracardiac filling pressures, because it is believed that hemodynamic congestion precedes clinical congestion. Monitoring intracardiac pressures may therefore enable clinicians to intervene and avert hospitalizations in a pre-symptomatic phase. Several techniques have been discussed in this review, and thus far, remote monitoring of pulmonary artery pressures (PAP) by the CardioMEMS (CardioMicroelectromechanical system) HF System is the only technique with proven safety as well as efficacy with regard to the prevention of HF-related hospital admissions. Efforts are currently aimed to further develop existing techniques and new sensors capable of measuring left atrial pressures (LAP). With the growing body of evidence and need for remote care, it is expected that remote monitoring by invasive sensors will play a larger role in HF care in the near future. [ABSTRACT FROM AUTHOR]

Sleep staging is important in sleep research since it is the basis for sleep evaluation and disease diagnosis. Related works have acquired many desirable outcomes. However, most of current studies focus on time-domain or frequency-domain measures as classification features using single or very few channels, which only obtain the local features but ignore the global information exchanging between different brain regions. Meanwhile, brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas. To explore the electroencephalography (EEG)-based brain mechanisms of sleep stages through functional connectivity, especially from different frequency bands, we applied phase-locked value (PLV) to build the functional connectivity network and analyze the brain interaction during sleep stages for different frequency bands. Then, we performed the feature-level, decision-level and hybrid fusion methods to discuss the performance of different frequency bands for sleep stages. The results show that (1) PLV increases in the lower frequency band (delta and alpha bands) and vice versa during different stages of non-rapid eye movement (NREM); (2) alpha band shows a better discriminative ability for sleeping stages; (3) the classification accuracy of feature-level fusion (six frequency bands) reaches 96.91% and 96.14% for intra-subject and inter-subjects respectively, which outperforms decision-level and hybrid fusion methods. [ABSTRACT FROM AUTHOR]

Motor imaging (MI) induces recovery and neuroplasticity in neurophysical regulation. However, a non-negligible portion of users presents insufficient coordination skills of sensorimotor cortex control. Assessments of the relationship between wakefulness and tasks states are conducted to foster neurophysiological and mechanistic interpretation in MI-related applications. Thus, to understand the organization of information processing, measures of functional connectivity are used. Also, models of neural network regression prediction are becoming popular, These intend to reduce the need for extracting features manually. However, predicting MI practicing's neurophysiological inefficiency raises several problems, like enhancing network regression performance because of the overfitting risk. Here, to increase the prediction performance, we develop a deep network regression model that includes three procedures: leave-one-out cross-validation combined with Monte Carlo dropout layers, subject clustering of MI inefficiency, and transfer learning between neighboring runs. Validation is performed using functional connectivity predictors extracted from two electroencephalographic databases acquired in conditions close to real MI applications (150 users), resulting in a high prediction of pretraining desynchronization and initial training synchronization with adequate physiological interpretability. [ABSTRACT FROM AUTHOR]

Contact-free sensors offer important advantages compared to traditional wearables. Radio-frequency sensors (e.g., radars) offer the means to monitor cardiorespiratory activity of people without compromising their privacy, however, only limited information can be obtained via movement, traditionally related to heart or breathing rate. We investigated whether five complex hemodynamics scenarios (resting, apnea simulation, Valsalva maneuver, tilt up and tilt down on a tilt table) can be classified directly from publicly available contact and radar input signals in an end-to-end deep learning approach. A series of robust k-fold cross-validation evaluation experiments were conducted in which neural network architectures and hyperparameters were optimized, and different data input modalities (contact, radar and fusion) and data types (time and frequency domain) were investigated. We achieved reasonably high accuracies of 88% for contact, 83% for radar and 88% for fusion of modalities. These results are valuable in showing large potential of radar sensing even for more complex scenarios going beyond just heart and breathing rate. Such contact-free sensing can be valuable for fast privacy-preserving hospital screenings and for cases where traditional werables are impossible to use. [ABSTRACT FROM AUTHOR]

Current telemedicine and remote healthcare applications foresee different interactions between the doctor and the patient relying on the use of commercial and medical wearable sensors and internet-based video conferencing platforms. Nevertheless, the existing applications necessarily require a contact between the patient and sensors for an objective evaluation of the patient's state. The proposed study explored an innovative video-based solution for monitoring neurophysiological parameters of potential patients and assessing their mental state. In particular, we investigated the possibility to estimate the heart rate (HR) and eye blinks rate (EBR) of participants while performing laboratory tasks by mean of facial—video analysis. The objectives of the study were focused on: (i) assessing the effectiveness of the proposed technique in estimating the HR and EBR by comparing them with laboratory sensor-based measures and (ii) assessing the capability of the video—based technique in discriminating between the participant's resting state (Nominal condition) and their active state (Non-nominal condition). The results demonstrated that the HR and EBR estimated through the facial—video technique or the laboratory equipment did not statistically differ (p > 0.1), and that these neurophysiological parameters allowed to discriminate between the Nominal and Non-nominal states (p < 0.02). [ABSTRACT FROM AUTHOR]

Among various methods for frequency recognition of the steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) study, a task-related component analysis (TRCA), which extracts discriminative spatial filters for classifying electroencephalogram (EEG) signals, has gathered much interest. The TRCA-based SSVEP method yields lower computational cost and higher classification performance compared to existing SSVEP methods. In spite of its utility, the TRCA-based SSVEP method still suffers from the degradation of the frequency recognition rate in cases where EEG signals with a short length window are used. To address this issue, here, we propose an improved strategy for decoding SSVEPs, which is insensitive to a window length by carrying out two-step TRCA. The proposed method reuses the spatial filters corresponding to target frequencies generated by the TRCA. Followingly, the proposed method accentuates features for target frequencies by correlating individual template and test data. For the evaluation of the performance of the proposed method, we used a benchmark dataset with 35 subjects and confirmed significantly improved performance comparing with other existing SSVEP methods. These results imply the suitability as an efficient frequency recognition strategy for SSVEP-based BCI applications. [ABSTRACT FROM AUTHOR]

Drowsiness while driving can lead to accidents that are related to the loss of perception during emergencies that harm the health. Among physiological signals, brain waves have been used as informative signals for the analyses of behavioral observations, steering information, and other biosignals during drowsiness. We inspected the machine learning methods for drowsiness detection based on brain signals with varying quantities of information. The results demonstrated that machine learning could be utilized to compensate for a lack of information and to account for individual differences. Cerebral area selection approaches to decide optimal measurement locations could be utilized to minimize the discomfort of participants. Although other statistics could provide additional information in further study, the optimized machine learning method could prevent the dangers of drowsiness while driving by considering a transitional state with nonlinear features. Because brain signals can be altered not only by mental fatigue but also by health status, the optimization analysis of the system hardware and software will be able to increase the power-efficiency and accessibility in acquiring brain waves for health enhancements in daily life. [ABSTRACT FROM AUTHOR]

Immersive virtual reality techniques have been applied to the rehabilitation of patients after stroke, but evidence of its clinical effectiveness is scarce. The present review aims to find studies that evaluate the effects of immersive virtual reality (VR) therapies intended for motor function rehabilitation compared to conventional rehabilitation in people after stroke and make recommendations for future studies. Data from different databases were searched from inception until October 2020. Studies that investigated the effects of immersive VR interventions on post-stroke adult subjects via a head-mounted display (HMD) were included. These studies included a control group that received conventional therapy or another non-immersive VR intervention. The studies reported statistical data for the groups involved in at least the posttest as well as relevant outcomes measuring functional or motor recovery of either lower or upper limbs. Most of the studies found significant improvements in some outcomes after the intervention in favor of the virtual rehabilitation group. Although evidence is limited, immersive VR therapies constitute an interesting tool to improve motor learning when used in conjunction with traditional rehabilitation therapies, providing a non-pharmacological therapeutic pathway for people after stroke. [ABSTRACT FROM AUTHOR]

Heart failure (HF) is a major health care issue, and the incidence of HF is only expected to grow further. Due to the frequent hospitalizations, HF places a major burden on the available hospital and healthcare resources. In the future, HF care should not only be organized solely at the clinical ward and outpatient clinics, but remote monitoring strategies are urgently needed to guide, monitor, and treat chronic HF patients remotely from their homes as well. The intuitiveness and relatively low costs of non-invasive remote monitoring tools make them an appealing and emerging concept for developing new medical apps and devices. The recent COVID-19 pandemic and the associated transition of patient care outside the hospital will boost the development of remote monitoring tools, and many strategies will be reinvented with modern tools. However, it is important to look carefully at the inconsistencies that have been reported in non-invasive remote monitoring effectiveness. With this review, we provide an up-to-date overview of the available evidence on non-invasive remote monitoring in chronic HF patients and provide future perspectives that may significantly benefit the broader group of HF patients. [ABSTRACT FROM AUTHOR]