EuroMov Digital Health in Motion

BACKGROUND: In the last 5 years since our last systematic review, a significant number of articles have been published on the technical aspects of muscle near-infrared spectroscopy (NIRS), the interpretation of the signals and the benefits of using the NIRS technique to measure the physiological status of muscles and to determine the workload of working muscles. OBJECTIVES: Considering the consistent number of studies on the application of muscle oximetry in sports science published over the last 5 years, the objectives of this updated systematic review were to highlight the applications of muscle oximetry in the assessment of skeletal muscle oxidative performance in sports activities and to emphasize how this technology has been applied to exercise and training over the last 5 years. In addition, some recent instrumental developments will be briefly summarized. METHODS: Preferred Reporting Items for Systematic Reviews guidelines were followed in a systematic fashion to search, appraise and synthesize existing literature on this topic. Electronic databases such as Scopus, MEDLINE/PubMed and SPORTDiscus were searched from March 2017 up to March 2023. Potential inclusions were screened against eligibility criteria relating to recreationally trained to elite athletes, with or without training programmes, who must have assessed physiological variables monitored by commercial oximeters or NIRS instrumentation. RESULTS: Of the identified records, 191 studies regrouping 3435 participants, met the eligibility criteria. This systematic review highlighted a number of key findings in 37 domains of sport activities. Overall, NIRS information can be used as a meaningful marker of skeletal muscle oxidative capacity and can become one of the primary monitoring tools in practice in conjunction with, or in comparison with, heart rate or mechanical power indices in diverse exercise contexts and across different types of training and interventions. CONCLUSIONS: Although the feasibility and success of the use of muscle oximetry in sports science is well documented, there is still a need for further instrumental development to overcome current instrumental limitations. Longitudinal studies are urgently needed to strengthen the benefits of using muscle oximetry in sports science.

Taking regular walks when living with Parkinson’s disease (PD) has beneficial effects on movement and quality of life. Yet, patients usually show reduced physical activity compared to healthy older adults. Using auditory stimulation such as music can facilitate walking but patients vary significantly in their response. An individualized approach adapting musical tempo to patients’ gait cadence, and capitalizing on these individual differences, is likely to provide a rewarding experience, increasing motivation for walk-in PD. We aim to evaluate the observance, safety, tolerance, usability, and enjoyment of a new smartphone application. It was coupled with wearable sensors (BeatWalk) and delivered individualized musical stimulation for gait auto-rehabilitation at home. Forty-five patients with PD underwent a 1-month, outdoor, uncontrolled gait rehabilitation program, using the BeatWalk application (30 min/day, 5 days/week). The music tempo was being aligned in real-time to patients’ gait cadence in a way that could foster an increase up to +10% of their spontaneous cadence. Open-label evaluation was based on BeatWalk use measures, questionnaires, and a six-minute walk test. Patients used the application 78.8% (±28.2) of the prescribed duration and enjoyed it throughout the program. The application was considered “easy to use” by 75% of the patients. Pain, fatigue, and falls did not increase. Fear of falling decreased and quality of life improved. After the program, patients improved their gait parameters in the six-minute walk test without musical stimulation. BeatWalk is an easy to use, safe, and enjoyable musical application for individualized gait rehabilitation in PD. It increases “walk for exercise” duration thanks to high observance. Clinical Trial Registration : ClinicalTrials.gov Identifier: NCT02647242.

BACKGROUND: Transcranial direct current stimulation (tDCS) has emerged as a promising and feasible method to improve motor performance in healthy and clinical populations. However, the potential of tDCS to enhance sport-specific motor performance in athletes remains elusive. OBJECTIVE: We aimed at analyzing the acute effects of a single anodal tDCS session on sport-specific motor performance changes in athletes compared to sham. METHODS: A systematic review and meta-analysis was conducted in the electronic databases PubMed, Web of Science, and SPORTDiscus. The meta-analysis was performed using an inverse variance method and a random-effects model. Additionally, two subgroup analyses were conducted (1) depending on the stimulated brain areas (primary motor cortex (M1), temporal cortex (TC), prefrontal cortex (PFC), cerebellum (CB)), and (2) studies clustered in subgroups according to different sports performance domains (endurance, strength, visuomotor skill). RESULTS: A total number of 19 studies enrolling a sample size of 258 athletes were deemed eligible for inclusion. Across all included studies, a significant moderate standardized mean difference (SMD) favoring anodal tDCS to enhance sport-specific motor performance could be observed. Subgroup analysis depending on cortical target areas of tDCS indicated a significant moderate SMD in favor of anodal tDCS compared to sham for M1 stimulation. CONCLUSION: A single anodal tDCS session can lead to performance enhancement in athletes in sport-specific motor tasks. Although no definitive conclusions can be drawn regarding the modes of action as a function of performance domain or stimulation site, these results imply intriguing possibilities concerning sports performance enhancement through anodal M1 stimulation.

OBJECTIVE: To apply a machine learning analysis to clinical and presynaptic dopaminergic imaging data of patients with rapid eye movement (REM) sleep behavior disorder (RBD) to predict the development of Parkinson disease (PD) and dementia with Lewy bodies (DLB). METHODS: In this multicenter study of the International RBD study group, 173 patients (mean age 70.5 ± 6.3 years, 70.5% males) with polysomnography-confirmed RBD who eventually phenoconverted to overt alpha-synucleinopathy (RBD due to synucleinopathy) were enrolled, and underwent baseline presynaptic dopaminergic imaging and clinical assessment, including motor, cognitive, olfaction, and constipation evaluation. For comparison, 232 RBD non-phenoconvertor patients (67.6 ± 7.1 years, 78.4% males) and 160 controls (68.2 ± 7.2 years, 53.1% males) were enrolled. Imaging and clinical features were analyzed by machine learning to determine predictors of phenoconversion. RESULTS: Machine learning analysis showed that clinical data alone poorly predicted phenoconversion. Presynaptic dopaminergic imaging significantly improved the prediction, especially in combination with clinical data, with 77% sensitivity and 85% specificity in differentiating RBD due to synucleinopathy from non phenoconverted RBD patients, and 85% sensitivity and 86% specificity in discriminating PD-converters from DLB-converters. Quantification of presynaptic dopaminergic imaging showed that an empirical z-score cutoff of -1.0 at the most affected hemisphere putamen characterized RBD due to synucleinopathy patients, while a cutoff of -1.0 at the most affected hemisphere putamen/caudate ratio characterized PD-converters. INTERPRETATION: Clinical data alone poorly predicted phenoconversion in RBD due to synucleinopathy patients. Conversely, presynaptic dopaminergic imaging allows a good prediction of forthcoming phenoconversion diagnosis. This finding may be used in designing future disease-modifying trials. ANN NEUROL 2024;95:1178-1192.

INTRODUCTION: Isolated/idiopathic rapid eye movement sleep behavior disorder (iRBD) is a powerful early predictor of dementia with Lewy bodies (DLB) and Parkinson's disease (PD). This provides an opportunity to directly observe the evolution of prodromal DLB and to identify which cognitive variables are the strongest predictors of evolving dementia. METHODS: IRBD participants (n = 754) from 10 centers of the International RBD Study Group underwent annual neuropsychological assessment. Competing risk regression analysis determined optimal predictors of dementia. Linear mixed-effect models determined the annual progression of neuropsychological testing. RESULTS: Reduced attention and executive function, particularly performance on the Trail Making Test Part B, were the strongest identifiers of early DLB. In phenoconverters, the onset of cognitive decline began up to 10 years prior to phenoconversion. Changes in verbal memory best differentiated between DLB and PD subtypes. DISCUSSION: In iRBD, attention and executive dysfunction strongly predict dementia and begin declining several years prior to phenoconversion. HIGHLIGHTS: Cognitive decline in iRBD begins up to 10 years prior to phenoconversion. Attention and executive dysfunction are the strongest predictors of dementia in iRBD. Decline in episodic memory best distinguished dementia-first from parkinsonism-first phenoconversion.

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BACKGROUND: The beneficial effects of acute exercise on executive function have been well-documented, but the influence of cardiorespiratory fitness on this effect requires further investigations, especially using imaging technique. This study aimed to examine the effects of cardiorespiratory fitness on acute exercise-induced changes on behavioral performance and on functional brain activation. METHOD: max) were finally selected and allocated to high-fit group or low-fit group. Both groups were asked to complete the Stroop task after 30 min of aerobic exercise and chair-seated rest (control session). Among them, 26 participants were randomly selected and asked to undergo the Functional Magnetic Resonance Imaging (fMRI). RESULTS: Behavioral results showed that individuals responded significantly faster after exercise than those in the control session. The fMRI results revealed a significant interaction effects of Group by Session in brain regions including anterior cingulate cortex (ACC) and bilateral dorsal lateral prefrontal cortex (DLPFC). For the ACC, activation in the high-fit group was significantly decreased after aerobic exercise compared to those in the control session; whereas an increased activation was noticed in the low-fit group. Regarding to the bilateral DLPFC, activation in high-fit group was significantly decreased after exercise compared to those in the control session, while no significant differences were found in the low-fit group. In addition, for the post-exercise session, a significant positive correlations between activation of the ACC and left DLPFC in the high-fit group was observed. There was a significant negative correlation between activation of the ACC and reaction time in the congruent condition after exercise in the low-fit group. CONCLUSION: Findings further clarify the neurophysiological processes of acute exercise-induced changes in cognitive performance as they suggest that cardiorespiratory fitness is an important factor which influences changes in brain activation patterns in response to acute aerobic exercises.

This study aims to provide a transferable methodology in the context of sport performance modelling, with a special focus to the generalisation of models. Data were collected from seven elite Short track speed skaters over a three months training period. In order to account for training load accumulation over sessions, cumulative responses to training were modelled by impulse, serial and bi-exponential responses functions. The variable dose-response (DR) model was compared to elastic net (ENET), principal component regression (PCR) and random forest (RF) models, while using cross-validation within a time-series framework. ENET, PCR and RF models were fitted either individually ([Formula: see text]) or on the whole group of athletes ([Formula: see text]). Root mean square error criterion was used to assess performances of models. ENET and PCR models provided a significant greater generalisation ability than the DR model ([Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] for [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text], respectively). Only [Formula: see text] and [Formula: see text] were significantly more accurate in prediction than DR ([Formula: see text] and [Formula: see text]). In conclusion, ENET achieved greater generalisation and predictive accuracy performances. Thus, building and evaluating models within a generalisation enhancing procedure is a prerequisite for any predictive modelling.

Voluntary force production requires that the brain produces and transmits a motor command to the muscles. It is widely acknowledged that motor commands are executed from the primary motor cortex (M1) located in the contralateral hemisphere. However, involvement of M1 located in the ipsilateral hemisphere during moderate to high levels of unilateral muscle contractions (>30% of the maximum) has been disclosed in recent years. This phenomenon has been termed cross-activation. The activation of the ipsilateral M1 relies on complex inhibitory and excitatory interhemispheric interactions mediated via the corpus callosum and modulated according to the contraction level. The regulatory mechanisms underlying these interhemispheric interactions, especially excitatory ones, remain vague, and contradictions exist in the literature. In addition, very little is known regarding the possibility that other pathways could also mediate the cross-activation. In the present review, we will therefore summarize the concept of cross-activation during unilateral voluntary muscle contraction and explore the associated mechanisms and other nervous system pathways underpinning this response. A broader knowledge of these mechanisms would consequently allow a better comprehension of the motor system as a whole, as distant brain networks working together to produce the motor command.

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The quality of arm movements typically improves in the sub-acute phase of stroke affecting the upper extremity. Here, we used whole arm kinematic analysis during reaching movements to distinguish whether these improvements are due to true recovery or to compensation. Fifty-three participants with post-acute stroke performed ∼80 reaching movement tests during 4 weeks of training with the ArmeoSpring exoskeleton. All participants showed improvements in end-effector performance, as measured by movement smoothness. Four ArmeoSpring angles, shoulder horizontal (SH) rotation, shoulder elevation (SE), elbow rotation, and forearm rotation, were recorded and analyzed. We first characterized healthy joint coordination patterns by performing a sparse principal component analysis on these four joint velocities recorded during reaching tests performed by young control participants. We found that two dominant joint correlations [SH with elbow rotation and SE with forearm rotation] explained over 95% of variance of joint velocity data. We identified two clusters of stroke participants by comparing the evolution of these two correlations in all tests. In the “Recoverer” cluster ( N = 19), both joint correlations converged toward the respective correlations for control participants. Thus, Recoverers relearned how to generate smooth end-effector movements while developing joint movement patterns similar to those of control participants. In the “Compensator” cluster ( N = 34), at least one of the two joint correlations diverged from the corresponding correlation of control participants. Compensators relearned how to generate smooth end-effector movements by discovering various new compensatory movement patterns dissimilar to those of control participants. New compensatory patterns included atypical decoupling of the SE and forearm joints, and atypical coupling of the SH rotation and elbow joints. There was no difference in clinical impairment level between the two groups either at the onset or at the end of training as assessed with the Upper Extremity Fugl-Meyer scale. However, at the start of training, the Recoverers showed significantly faster improvements in end-effector movement smoothness than the Compensators. Our analysis can be used to inform neurorehabilitation clinicians on how to provide movement feedback during practice and suggest avenues for refining exoskeleton robot therapy to reduce compensatory patterns.

ABSTRACT: Clinical models of chronic low back pain (cLBP) highlight the role of excessive attention to pain and kinesiophobia on the origin of disability. At the motor control level, various mechanisms are involved in the impairments observed in patients with cLBP. We aimed to assess the role of maladaptative attentional behaviors by using a complex systems approach and a visual display as a distraction during walking. Sixteen patients with cLBP with no previous surgery or significant leg pain and 16 healthy matched controls were included. Patients walked on a treadmill at preferred walking speed with and without distraction. Stride time (ST) fractal complexity was assessed using detrended fluctuation analysis. A two-way analysis of variance with repeated measures on distraction was performed on fractal exponents. We found a significant group × distraction interaction effect on fractal complexity of ST series (F(1,30) = 9.972, P = 0.004). Post hoc analysis showed that, without distraction, patients with cLBP had significantly lower ST complexity than controls, but when distracted, they regained gait complexity, recovering the level of controls. Our results suggest that excessive attention to pain causes loss of complexity and adaptability in cLBP and explain alterations of motor control with pain. Fractal analysis seems to be a promising method to explore movement variability and individual adaptability in musculoskeletal disorders.

The recent COVID-19 pandemic has highlighted all the weaknesses of manufacturing systems and supply chains. In this challenging context, smallholders have faced several crises mainly related to the difficulty of finding manpower for harvesting activities and the impossibility of distributing food, due to the forced closure of many distribution channels. The main consequences were lost sales and wasted food. With the aim of increasing the responsiveness of smallholders in the face of COVID-like crises, this paper provides an overview of methodologies and approaches currently available in the literature in terms of: ICT tools, blockchain-based solutions, business models, sustainability-oriented frameworks, simulation models. The analysis of the literature provides two main outputs: (1) a list of challenges to be faced in the coming years to improve the working conditions of smallholders, (2) the definition of future research developments, which mainly concern the design of an ICT platform, which integrates multiple technological aspects.

The diversity of the item list suggested by recommender systems has been proven to impact user satisfaction significantly. Most of the existing diversity optimization approaches re-rank the list of candidate items during a post-processing step. However, the diversity level of the candidate list strongly depends on the recommender system used. Hence, applying the same post-processing diversification strategy may not be as effective for different recommendation approaches. Moreover, individual users’ diversity needs are usually ignored in the diversification post-processing. This article aims at providing an in-depth analysis of the diversity performances of different recommender systems. To the best of our knowledge, it is the first study to systematically compare diversity performances of the main types of recommendation models using benchmark datasets in different domains (movie, anime and book). Semantics related to items may be considered a key factor in measuring diversity within recommender systems. In this study, we leverage support from the knowledge engineering domain and take advantage of resources such as Linked Data and knowledge graphs, to assert the diversity of recommendations. We also propose a variant of the classic diversification post-processing objective that allows to take into account specific users’ diversity needs. We measure the adequacy between the diversity levels a recommender system suggests to its users and those of users’ profiles with the R2 coefficient of determination. Our study indicates that: (1) none of the tested recommender systems, even the most recent ones, provides items with levels of diversity that suit user profiles (R2<0.2); (2) the classic post-processing diversification approach may lead to over-diversification compared to users’ diversity needs and (3) the diversity adjustment that accounts for user profiles has more benefits (greater R2 and smaller accuracy loss). All the source code and datasets used in our study are available to ensure the reproductibility of the study.

In a recent article published in The Journal of Neurophysiology titled “Sensitivity to changes in rate of heartbeats as a measure of interoceptive ability,” Larsson et al. ( J Neurophysiol 126: 1799–1813, 2021) introduce a new method to evaluate the interoceptive ability and report a surprising tendency in humans to perceive fewer heartbeats during spontaneous increases in resting heart rate. The authors argue that this result reflects a reduction in the strength of the heartbeat during the inspiration periods. Here, we discuss this finding and propose a complementary interpretation grounded on consciousness research and an emerging literature showing the influence of the breathing phase on perception and brain activity at rest.

The objective of this study was to validate PLATES for assessing unipodal balance in the field, for example, to monitor ankle instabilities in athletes or patients. PLATES is a pair of lightweight, connected force platforms that measure only vertical forces. In 14 healthy women, we measured ground reaction forces during Single Leg Balance and Single Leg Landing tests, first under laboratory conditions (with PLATES and with a 6-DOF reference force platform), then during a second test session in the field (with PLATES). We found that for these simple unipodal balance tests, PLATES was reliable in the laboratory and in the field: PLATES gives results comparable with those of a reference force platform with 6-DOF for the key variables in the tests (i.e., Mean Velocity of the Center of Pressure and Time to Stabilization). We conclude that health professionals, physical trainers, and researchers can use PLATES to conduct Single Leg Balance and Single Leg Landing tests in the laboratory and in the field.

Emotions are a natural vector for acting together with others and are witnessed in human behaviour, perception and body functions. For this reason, studies of human-to-human interaction, such as multi-person motor synchronisation, are a perfect setting to disentangle the linkage of emotion with socio-motor interaction. And yet, the majority of joint action studies aiming at understanding the impact of emotions on multi-person performance resort to enacted emotions, the ones that are emulated based on the previous experience of such emotions, and almost exclusively focus on dyadic interaction. In addition, tasks chosen to study emotion in joint action are frequently characterised by a reduced number of physical dimensions to gain experimental control and subsequent facilitation in data analysis. Therefore, it is not clear how naturalistically induced emotions diffuse in more ecological interactions with other people and how emotions affect the process of interpersonal synchronisation. Here, we show that positive and negative emotions differently alter spontaneous human synchronous behaviour during a multi-person improvisation task. The study involved 39 participants organised in triads who self-reported liking improvisational activities (e.g., dancing). The task involved producing improvisational movements with the right hand. Participants were emotionally induced by manipulated social feedback involving a personal ranking score. Three-dimensional spatio-temporal data and cardiac activity were extracted and transformed into oscillatory signals (phases) to compute behavioural and physiological synchrony. Our results demonstrate that individuals induced with positive emotions, as opposed to negative emotions or a neutral state, maintained behavioural synchrony with other group members for a longer period of time. These findings contribute to the emerging shift of neuroscience of emotion and affective sciences towards the environment of social significance where emotions appear the most-in interaction with others. Our study showcases a method of quantification of synchrony in an improvisational and interactive task based on a well-established Kuramoto model.

Interest for neuromodulation, and transcranial random noise stimulation (tRNS) in particular, is growing. It concerns patients rehabilitation, but also healthy people who want or need to improve their cognitive and learning abilities. However, there is no consensus yet regarding the efficacy of tRNS on learning and performing a complex task. In particular, the most effective electrode montage is yet to be determined. Here, we examined the effect of two different tRNS montages on learning rate, short- and long-term performance in a video game (Space Fortress) that engages multiple cognitive abilities. Sixty-one participants were randomly assigned to one of three groups (sham vs. simple-definition tRNS vs. high-definition tRNS) in a double-blind protocol. Their performance on the Space Fortress task was monitored during a 15-day experiment with baseline (day 1), stimulation (day 2 to 4), short- (day 5) and long-term (day 15) evaluations. Our results show that the high-definition tRNS group improved more on the long term than simple-definition tRNS group, tended to learn faster and had better performance retention compared to both simple-definition tRNS and sham groups. This study is the first to report that high-definition tRNS is more effective than conventional simple-definition tRNS to enhance performance in a complex task.

Introduction: Dementia is a neurological disorder associated with aging that can cause a loss of cognitive functions, impacting daily life. Alzheimer's disease (AD) is the most common cause of dementia, accounting for 50-70% of cases, while frontotemporal dementia (FTD) affects social skills and personality. Electroencephalography (EEG) provides an effective tool to study the effects of AD on the brain. Methods: In this study, we propose to use shallow neural networks applied to two sets of features: spectral-temporal and functional connectivity using four methods. We compare three supervised machine learning techniques to the CNN models to classify EEG signals of AD / FTD and control cases. We also evaluate different measures of functional connectivity from common EEG frequency bands considering multiple thresholds. Results and discussion: Results showed that the shallow CNN-based models achieved the highest accuracy of 94.54% with AEC in test dataset when considering all connections, outperforming conventional methods and providing potentially an additional early dementia diagnosis tool.

Digital twins, along with Internet of Things and Artificial Intelligence, have been identified as one of the key technologies for Industry 4.0. However, the definition of Digital Twin (DT) is still abstract and context-dependent. In this paper, we present a metamodel that supports concrete and operational descriptions of digital twin deployment. This metamodel encompasses the different aspects of deployment, including the definition of hardware and software components that compose the layered cyber-physical architectures of the digital twin, along with the installation and instantiation tasks that compose deployment processes. Multiple configurations can also be defined to support the deployment of a digital twin in different execution contexts. The relevance of this metamodel was evaluated by two case studies. The first consists in deploying the digital twin of a cobot in a simulation environment. The second applies the approach in a home automation environment. In both cases, our metamodel provides complete and precise descriptions of the deployment process and thus constitutes a viable first step towards a model-driven approach for digital twin deployment.