Assessment of the Barriers and Enablers of the Use of mHealth Systems in Sub-Saharan Africa According to the Perceptions of Patients, Physicians, and Health Care Executives in Ethiopia: Qualitative Study.

BACKGROUND: Digital technologies are increasingly being used to deliver health care services and promote public health. Mobile wireless technologies or mobile health (mHealth) technologies are particularly relevant owing to their ease of use, broad reach, and wide acceptance. Unlike developed countries, Sub-Saharan Africa experiences more challenges and obstacles when it comes to deploying, using, and expanding mHealth systems. In addition to barriers, there are enabling factors that could be exploited for the design, implementation, and scaling up of mHealth systems. Sub-Saharan Africa may require tailored solutions that address the specific challenges facing the region. OBJECTIVE: The overall aim of this study was to identify the barriers and enablers for using mHealth systems in Sub-Saharan Africa from the perspectives of patients, physicians, and health care executives. METHODS: Multi-level and multi-actor in-depth semistructured interviews were employed to qualitatively explore the barriers and enablers of the use of mHealth systems. Data were collected from patients, physicians, and health care executives. The interviews were audio recorded, transcribed verbatim, translated, and coded. Thematic analysis methodology was adopted, and NVivo software was used for the data analysis. RESULTS: Through this rigorous study, a total of 137 determinants were identified. Of these determinants, 68 were identified as barriers and 69 were identified as enablers. Perceived barriers in patients included lack of awareness about mHealth systems and language barriers. Perceived enablers in patients included need for automated tools for health monitoring and an increasing literacy level of the society. According to physicians, barriers included lack of available digital health systems in the local context and concern about patients' mHealth capabilities, while enablers included the perceived usefulness in reducing workload and improving health care service quality, as well as the availability of mobile devices and the internet. As perceived by health care executives, barriers included competing priorities alongside digitalization in the health sector and lack of interoperability and complete digitalization of implemented digital health systems, while enablers included the perceived usefulness of digitalization for the survival of the highly overloaded health care system and the abundance of educated manpower specializing in technology. CONCLUSIONS: mHealth systems in Sub-Saharan Africa are hindered and facilitated by various factors. Common barriers and enablers were identified by patients, physicians, and health care executives. To promote uptake, all relevant stakeholders must actively mitigate the barriers. This study identified a promising outlook for mHealth in Sub-Saharan Africa, despite the present barriers. Opportunities exist for successful integration into health care systems, and a user-centered design is crucial for maximum uptake.

Reliability and Discriminative Validity of Wearable Sensors for the Quantification of Upper Limb Movement Disorders in Individuals with Dyskinetic Cerebral Palsy.

Background-Movement patterns in dyskinetic cerebral palsy (DCP) are characterized by abnormal postures and involuntary movements. Current evaluation tools in DCP are subjective and time-consuming. Sensors could yield objective information on pathological patterns in DCP, but their reliability has not yet been evaluated. The objectives of this study were to evaluate (i) reliability and (ii) discriminative ability of sensor parameters. Methods-Inertial measurement units were placed on the arm, forearm, and hand of individuals with and without DCP while performing reach-forward, reach-and-grasp-vertical, and reach-sideways tasks. Intra-class correlation coefficients (ICC) were calculated for reliability, and Mann-Whitney U-tests for between-group differences. Results-Twenty-two extremities of individuals with DCP (mean age 16.7 y) and twenty individuals without DCP (mean age 17.2 y) were evaluated. ICC values for all sensor parameters except jerk and sample entropy ranged from 0.50 to 0.98 during reach forwards/sideways and from 0.40 to 0.95 during reach-and-grasp vertical. Jerk and maximal acceleration/angular velocity were significantly higher for the DCP group in comparison with peers. Conclusions-This study was the first to assess the reliability of sensor parameters in individuals with DCP, reporting high between- and within-session reliability for the majority of the sensor parameters. These findings suggest that pathological movements of individuals with DCP can be reliably captured using a selection of sensor parameters.

A modelling framework for local thermal comfort assessment related to bicycle helmet use.

Thermal discomfort due to accumulated sweat increasing head skin wettedness may contribute to low wearing rates of bicycle helmets. Using curated data on human head sweating and helmet thermal properties, a modelling framework for the thermal comfort assessment of bicycle helmet use is proposed. Local sweat rates (LSR) at the head were predicted as the ratio to the gross sweat rate (GSR) of the whole body or by sudomotor sensitivity (SUD), the change in LSR per change in body core temperature (Δtre). Combining those local models with Δtre and GSR output from thermoregulation models, we simulated head sweating depending on the characteristics of the thermal environment, clothing, activity, and exposure duration. Local thermal comfort thresholds for head skin wettedness were derived in relation to thermal properties of bicycle helmets. The modelling framework was supplemented by regression equations predicting the wind-related reductions in thermal insulation and evaporative resistance of the headgear and boundary air layer, respectively. Comparing the predictions of local models coupled with different thermoregulation models to LSR measured at the frontal, lateral and medial head under bicycle helmet use revealed a large spread in LSR predictions predominantly determined by the local models and the considered head region. SUD tended to overestimate frontal LSR but performed better for lateral and medial head regions, whereas predictions by LSR/GSR ratios were lower and agreed better with measured frontal LSR. However, even for the best models root mean squared prediction errors exceeded experimental SD by 18-30%. From the high correlation (R > 0.9) of skin wettedness comfort thresholds with local sweating sensitivity reported for different body regions, we derived a threshold value of 0.37 for head skin wettedness. We illustrate the application of the modelling framework using a commuter-cycling scenario, and discuss its potential as well as the needs for further research.

Eye movements and stress during eye-tracking gaming performance in children with dyskinetic cerebral palsy.

AIM: This study aimed to explore eye movements and stress during eye-tracking gaming performance in children with dyskinetic cerebral palsy (CP) compared with typically developing children, and associations between eye-tracking performance, eye movements, stress, and participants' characteristics. METHOD: This cohort study included 12 children with dyskinetic CP aged 5 to 12 years (mean age 8 years 7 months, standard deviation [SD] 2 years 3 months) and 23 typically developing children aged 5 to 13 years (mean age 9 years 0 months, SD 2 years 7 months). Participants played 10 eye-tracking games. Tobii X3-120 and Tobii Pro Lab were used to record and analyse eye movements. Stress was assessed through heart rate variability (HRV), recorded during rest, and eye-tracking performance using the Bittium Faros360° ECG Holter device. Eye-tracking performance was measured using gaming completion time. Fixation and saccade variables were used to quantify eye movements, and time- and frequency-domain variables to quantify HRV. Non-parametric statistics were used. RESULTS: Gaming completion time was significantly different (p < 0.001) between groups, and it was negatively correlated with experience (rs  = -0.63, p = 0.029). No significant differences were found between groups in fixation and saccade variables. HRV significantly changed from rest to eye-tracking performance only in typically developing children and not in children with dyskinetic CP. INTERPRETATION: Children with dyskinetic CP took longer to perform the 10 games, especially the inexperienced users, indicating the importance of the early provision of eye-tracking training opportunities. It seems that eye-tracking tasks are not a source of increased stress and effort in children with dyskinetic CP. WHAT THIS PAPER ADDS: Participants with dyskinetic cerebral palsy (CP) took twice as long to perform 10 eye-tracking games than typically developing peers. Participants with dyskinetic CP with previous eye-tracking experience performed the games faster. Fixation and saccade variables were not significantly different between children with and without dyskinetic CP. Heart rate variability showed no differences between rest and performance in participants with dyskinetic CP. Gross Motor Function Classification System, Manual Ability Classification System, and Viking Speech Scale levels were not correlated to the eye movements or stress variables.

Vital Signs Prediction for COVID-19 Patients in ICU.

This study introduces machine learning predictive models to predict the future values of the monitored vital signs of COVID-19 ICU patients. The main vital sign predictors include heart rate, respiration rate, and oxygen saturation. We investigated the performances of the developed predictive models by considering different approaches. The first predictive model was developed by considering the following vital signs: heart rate, blood pressure (systolic, diastolic and mean arterial, pulse pressure), respiration rate, and oxygen saturation. Similar to the first approach, the second model was developed using the same vital signs, but it was trained and tested based on a leave-one-subject-out approach. The third predictive model was developed by considering three vital signs: heart rate (HR), respiration rate (RR), and oxygen saturation (SpO2). The fourth model was a leave-one-subject-out model for the three vital signs. Finally, the fifth predictive model was developed based on the same three vital signs, but with a five-minute observation rate, in contrast with the aforementioned four models, where the observation rate was hourly to bi-hourly. For the five models, the predicted measurements were those of the three upcoming observations (on average, three hours ahead). Based on the obtained results, we observed that by limiting the number of vital sign predictors (i.e., three vital signs), the prediction performance was still acceptable, with the average mean absolute percentage error (MAPE) being 12%,5%, and 21.4% for heart rate, oxygen saturation, and respiration rate, respectively. Moreover, increasing the observation rate could enhance the prediction performance to be, on average, 8%,4.8%, and 17.8% for heart rate, oxygen saturation, and respiration rate, respectively. It is envisioned that such models could be integrated with monitoring systems that could, using a limited number of vital signs, predict the health conditions of COVID-19 ICU patients in real-time.

Deep-learning algorithm helps to standardise ATS/ERS spirometric acceptability and usability criteria.

RATIONALE: While American Thoracic Society (ATS)/European Respiratory Society (ERS) quality control criteria for spirometry include several quantitative limits, it also requires manual visual inspection. The current approach is time consuming and leads to high intertechnician variability. We propose a deep-learning approach called convolutional neural network (CNN), to standardise spirometric manoeuvre acceptability and usability. METHODS AND METHODS: In 36 873 curves from the National Health and Nutritional Examination Survey USA 2011-2012, technicians labelled 54% of curves as meeting ATS/ERS 2005 acceptability criteria with satisfactory start and end of test, but identified 93% of curves with a usable forced expiratory volume in 1 s. We processed raw data into images of maximal expiratory flow-volume curve (MEFVC), calculated ATS/ERS quantifiable criteria and developed CNNs to determine manoeuvre acceptability and usability on 90% of the curves. The models were tested on the remaining 10% of curves. We calculated Shapley values to interpret the models. RESULTS: In the test set (n=3738), CNN showed an accuracy of 87% for acceptability and 92% for usability, with the latter demonstrating a high sensitivity (92%) and specificity (96%). They were significantly superior (p<0.0001) to ATS/ERS quantifiable rule-based models. Shapley interpretation revealed MEFVC<1 s (MEFVC pattern within first second of exhalation) and plateau in volume-time were most important in determining acceptability, while MEFVC<1 s entirely determined usability. CONCLUSION: The CNNs identified relevant attributes in spirometric curves to standardise ATS/ERS manoeuvre acceptability and usability recommendations, and further provides individual manoeuvre feedback. Our algorithm combines the visual experience of skilled technicians and ATS/ERS quantitative rules in automating the critical phase of spirometry quality control.

Residential green space and medication sales for childhood asthma: A longitudinal ecological study in Belgium.

BACKGROUND: Living in green environments has been associated with various health benefits, but the evidence for positive effects on respiratory health in children is ambiguous. OBJECTIVE: To investigate if residential exposure to different types of green space is associated with childhood asthma prevalence in Belgium. METHODS: Asthma prevalence was estimated from sales data of reimbursed medication for obstructive airway disease (OAD) prescribed to children between 2010 and 2014, aggregated at census tract level (n = 1872) by sex and age group (6-12 and 13-18 years). Generalized log-linear mixed effects models with repeated measures were used to estimate effects of relative covers of forest, grassland and garden in the census tract of the residence on OAD medication sales. Models were adjusted for air pollution (PM10), housing quality and administrative region. RESULTS: Consistent associations between OAD medication sales and relative covers of grassland and garden were observed (unadjusted parameter estimates per IQR increase of relative cover, range across four strata: grassland, ß = 0.15-0.17; garden, ß = 0.13-0.17). The associations remained significant after adjusting for housing quality and chronic air pollution (adjusted parameter estimates per IQR increase of relative cover, range across four strata: grassland, ß = 0.10-0.14; garden, ß = 0.07-0.09). There was no association between OAD medication sales and forest cover. CONCLUSIONS: Based on aggregated data, we found that living in close proximity to areas with high grass cover (grasslands, but also residential gardens) may negatively impact child respiratory health. Potential allergic and non-allergic mechanisms that underlie this association include elevated exposure to grass pollen and fungi and reduced exposure to environmental biodiversity. Reducing the dominance of grass in public and private green space might be beneficial to reduce the childhood asthma burden and may simultaneously improve the ecological value of urban green space.

Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology.

In this prospective, interventional, international study, we investigate continuous monitoring of hospitalised patients' vital signs using wearable technology as a basis for real-time early warning scores (EWS) estimation and vital signs time-series prediction. The collected continuous monitored vital signs are heart rate, blood pressure, respiration rate, and oxygen saturation of a heterogeneous patient population hospitalised in cardiology, postsurgical, and dialysis wards. Two aspects are elaborated in this study. The first is the high-rate (every minute) estimation of the statistical values (e.g., minimum and mean) of the vital signs components of the EWS for one-minute segments in contrast with the conventional routine of 2 to 3 times per day. The second aspect explores the use of a hybrid machine learning algorithm of kNN-LS-SVM for predicting future values of monitored vital signs. It is demonstrated that a real-time implementation of EWS in clinical practice is possible. Furthermore, we showed a promising prediction performance of vital signs compared to the most recent state of the art of a boosted approach of LSTM. The reported mean absolute percentage errors of predicting one-hour averaged heart rate are 4.1, 4.5, and 5% for the upcoming one, two, and three hours respectively for cardiology patients. The obtained results in this study show the potential of using wearable technology to continuously monitor the vital signs of hospitalised patients as the real-time estimation of EWS in addition to a reliable prediction of the future values of these vital signs is presented. Ultimately, both approaches of high-rate EWS computation and vital signs time-series prediction is promising to provide efficient cost-utility, ease of mobility and portability, streaming analytics, and early warning for vital signs deterioration.

Development of a Data Logger for Capturing Human-Machine Interaction in Wheelchair Head-Foot Steering Sensor System in Dyskinetic Cerebral Palsy.

The use of data logging systems for capturing wheelchair and user behavior has increased rapidly over the past few years. Wheelchairs ensure more independent mobility and better quality of life for people with motor disabilities. Especially, for people with complex movement disorders, such as dyskinetic cerebral palsy (DCP) who lack the ability to walk or to handle objects, wheelchairs offer a means of integration into daily life. The mobility of DCP patients is based on a head-foot wheelchair steering system. In this work, a data logging system is proposed to capture data from human-wheelchair interaction for the head-foot steering system. Additionally, the data logger provides an interface to multiple Inertial Measurement Units (IMUs) placed on the body of the wheelchair user. The system provides accurate and real-time information from head-foot navigation system pressure sensors on the wheelchair during driving. This system was used as a tool to obtain further insights into wheelchair control and steering behavior of people diagnosed with DCP in comparison with a healthy subject.

Silicone Wristband Passive Samplers Yield Highly Individualized Pesticide Residue Exposure Profiles.

Monitoring human exposure to pesticides and pesticide residues (PRs) remains crucial for informing public health policies, despite strict regulation of plant protection product and biocide use. We used 72 low-cost silicone wristbands as noninvasive passive samplers to assess cumulative 5-day exposure of 30 individuals to polar PRs. Ethyl acetate extraction and LC-MS/MS analysis were used for the identification of PRs. Thirty-one PRs were detected of which 15 PRs (48%) were detected only in worn wristbands, not in environmental controls. The PRs included 16 fungicides (52%), 8 insecticides (26%), 2 herbicides (6%), 3 pesticide derivatives (10%), 1 insect repellent (3%), and 1 pesticide synergist (3%). Five detected pesticides were not approved for plant protection use in the EU. Smoking and dietary habits that favor vegetable consumption were associated with higher numbers and higher cumulative concentrations of PRs in wristbands. Wristbands featured unique PR combinations. Our results suggest both environment and diet contributed to PR exposure in our study group. Silicone wristbands could serve as sensitive passive samplers to screen population-wide cumulative dietary and environmental exposure to authorized, unauthorized and banned pesticides.

Field Potential Oscillations in the Bed Nucleus of the Stria Terminalis Correlate with Compulsion in a Rat Model of Obsessive-Compulsive Disorder.

UNLABELLED: The bed nucleus of the stria terminalis (BNST) is implicated in anxiety and reward processing, both of which are associated with obsessive-compulsive disorder (OCD). Specific neuronal groups in the BNST related to anxiety and reward have been identified, but quantitative data about the information carried by local field potential (LFP) signals in this area during obsession/compulsion are lacking. Here we investigate the BNST LFP in the schedule-induced polydipsia, an animal model of OCD. We implanted electrodes bilaterally in the BNST and random control brain regions in 32 male Wistar rats, and recorded corresponding LFP during compulsive and noncompulsive behavior. We first applied high-frequency (100 Hz) electrical stimulation through the implanted electrodes and analyzed its effects on compulsive behavior. We then performed time-frequency analysis of LFPs and statistically compared the normalized power of δ (1-4 Hz), θ (4-8 Hz), α (8-12 Hz), ß (12-30 Hz), and lower γ (30-45 Hz) bands between different groups. Our data showed that the normalized δ, ß, and γ powers in the right BNST were specifically correlated with compulsive behaviors. δ and γ oscillations increased and decreased during the initiation phase of compulsion, respectively, whereas ß increased after compulsion stopped. Moreover, the effect of BNST electrical stimulation, in terms of suppression of compulsion, was significantly correlated with the percentage change of these bands during compulsion. Our research reveals potential biomarkers and underlying neurophysiological mechanisms of compulsion and warrants further assessment of the use of LFP for closed-loop neuromodulation in OCD. SIGNIFICANCE STATEMENT: Although specific neuronal groups in the bed nucleus of the stria terminalis (BNST) related to anxiety and reward circuitries have been identified, psychopathological information carried by local field potentials in the BNST has not yet been described. We discovered that normalized powers of the right BNST δ, ß, and γ oscillations were highly correlated with compulsion. Specifically, δ and γ oscillations increased and decreased during the initiation phase of compulsion, respectively, whereas ß increased after compulsion stopped. Such correlations were not found in other parts of the brain during compulsion, or in the BNST during noncompulsive behavior. Current findings reveal real-time neurophysiological biomarkers of compulsion and warrant further assessment of the use of local field potentials for closed-loop neuromodulation for OCD.

Non-linear parameters of specific resistance loops to characterise obstructive airways diseases.

BACKGROUND: Specific resistance loops appear in different shapes influenced by different resistive properties of the airways, yet their descriptive ability is compressed to a single parameter - its slope. We aimed to develop new parameters reflecting the various shapes of the loop and to explore their potential in the characterisation of obstructive airways diseases. METHODS: Our study included 134 subjects: Healthy controls (N = 22), Asthma with non-obstructive lung function (N = 22) and COPD of all disease stages (N = 90). Different shapes were described by geometrical and second-order transfer function parameters. RESULTS: Our parameters demonstrated no difference between asthma and healthy controls groups, but were significantly different (p < 0.0001) from the patients with COPD. Grouping mild COPD subjects by an open or not-open shape of the resistance loop revealed significant differences of loop parameters and classical lung function parameters. Multiple logistic regression indicated RV/TLC as the only predictor of loop opening with OR = 1.157, 95% CI (1.064-1.267), p-value = 0.0006 and R2 = 0.35. Inducing airway narrowing in asthma gave equal shape measures as in COPD non-openers, but with a decreased slope (p < 0.0001). CONCLUSION: This study introduces new parameters calculated from the resistance loops which may correlate with different phenotypes of obstructive airways diseases.

Automated Interpretation of Pulmonary Function Tests in Adults with Respiratory Complaints.

BACKGROUND: The use of pulmonary function tests is primarily based on expert opinion and international guidelines. Current interpretation strategies are using predefined cutoffs for the description of a typical pattern. OBJECTIVES: We aimed to explore the predicted disease outcome based on the American Thoracic Society/European Respiratory Society (ATS/ERS) interpreting strategy. Subsequently, we investigated whether an unbiased machine learning framework integrating lung function with clinical variables may provide alternative decision trees resulting in a more accurate diagnosis. METHODS: Our study included data from 968 subjects admitted for the first time to a pulmonary practice. The final clinical diagnosis was based on the combination of complete pulmonary function with the investigations that were decided at the physician's discretion. Clinical diagnoses were separated into 10 different groups and validated by an expert panel. RESULTS: The ATS/ERS algorithm resulted in a correct diagnostic label in 38% of the subjects. Chronic obstructive pulmonary disease (COPD) was detected with an acceptable accuracy (74%), whereas all other diseases were poorly identified. The new data-based decision tree improved the general accuracy to 68% after 10-fold cross-validation when detecting the most common lung diseases, with a significantly higher positive predictive value and sensitivity for COPD, asthma, interstitial lung disease, and neuromuscular disorder (83/78, 66/82, 52/59, and 100/54%, respectively). CONCLUSIONS: Our data show that the current algorithms for lung function interpretation can be improved by a computer-based choice of lung function and clinical variables and their decision-making thresholds.

Large-scale progenitor cell expansion for multiple donors in a monitored hollow fibre bioreactor.

BACKGROUND AIMS: With the increasing scale in stem cell production, a robust and controlled cell expansion process becomes essential for the clinical application of cell-based therapies. The objective of this work was the assessment of a hollow fiber bioreactor (Quantum Cell Expansion System from Terumo BCT) as a cell production unit for the clinical-scale production of human periosteum derived stem cells (hPDCs). METHODS: We aimed to demonstrate comparability of bioreactor production to standard culture flask production based on a product characterization in line with the International Society of Cell Therapy in vitro benchmarks and supplemented with a compelling quantitative in vivo bone-forming potency assay. Multiple process read-outs were implemented to track process performance and deal with donor-to-donor-related variation in nutrient needs and harvest timing. RESULTS: The data show that the hollow fiber bioreactor is capable of robustly expanding autologous hPDCs on a clinical scale (yield between 316 million and 444 million cells starting from 20 million after ± 8 days of culture) while maintaining their in vitro quality attributes compared with the standard flask-based culture. The in vivo bone-forming assay on average resulted in 10.3 ± 3.7% and 11.0 ± 3.8% newly formed bone for the bioreactor and standard culture flask respectively. The analysis showed that the Quantum system provides a reproducible cell expansion process in terms of yields and culture conditions for multiple donors.

Identifying time-varying dynamics of heart rate and oxygen uptake from single ramp incremental running tests.

Objective.The fact that ramp incremental exercise yields quasi-linear responses for pulmonary oxygen uptake (V˙O2) and heart rate (HR) seems contradictory to the well-known non-linear behavior of underlying physiological processes. Prior research highlights this issue and demonstrates how a balancing of system gain and response time parameters causes linearV˙O2responses during ramp tests. This study builds upon this knowledge and extracts the time-varying dynamics directly from HR andV˙O2data of single ramp incremental running tests.Approach.A large-scale open access dataset of 735 ramp incremental running tests is analyzed. The dynamics are obtained by means of 1st order autoregressive and exogenous models with time-variant parameters. This allows for the estimates of time constant (τ) and steady state gain (SSG) to vary with work rate.Main results.As the work rate increases,τ-values increase on average from 38 to 132 s for HR, and from 27 to 35 s forV˙O2. Both increases are statistically significant (p< 0.01). Further, SSG-values decrease on average from 14 to 9 bpm (km·h-1)-1for HR, and from 218 to 144 ml·min-1forV˙O2(p< 0.01 for decrease parameters of HR andV˙O2). The results of this modeling approach are line with literature reporting on cardiorespiratory dynamics obtained using standard procedures.Significance.We show that time-variant modeling is able to determine the time-varying dynamics HR andV˙O2responses to ramp incremental running directly from individual tests. The proposed method allows for gaining insights into the cardiorespiratory response characteristics when no repeated measurements are available.

Accuracy of Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP Versus Polysomnography: Systematic Review.

Background: Despite being the gold-standard method for objectively assessing sleep, polysomnography (PSG) faces several limitations as it is expensive, time-consuming, and labor-intensive; requires various equipment and technical expertise; and is impractical for long-term or in-home use. Consumer wrist-worn wearables are able to monitor sleep parameters and thus could be used as an alternative for PSG. Consequently, wearables gained immense popularity over the past few years, but their accuracy has been a major concern. Objective: A systematic review of the literature was conducted to appraise the performance of 3 recent-generation wearable devices (Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP) in determining sleep parameters and sleep stages. Methods: Per the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement, a comprehensive search was conducted using the PubMed, Web of Science, Google Scholar, Scopus, and Embase databases. Eligible publications were those that (1) involved the validity of sleep data of any marketed model of the candidate wearables and (2) used PSG or an ambulatory electroencephalogram monitor as a reference sleep monitoring device. Exclusion criteria were as follows: (1) incorporated a sleep diary or survey method as a reference, (2) review paper, (3) children as participants, and (4) duplicate publication of the same data and findings. Results: The search yielded 504 candidate articles. After eliminating duplicates and applying the eligibility criteria, 8 articles were included. WHOOP showed the least disagreement relative to PSG and Sleep Profiler for total sleep time (-1.4 min), light sleep (-9.6 min), and deep sleep (-9.3 min) but showed the largest disagreement for rapid eye movement (REM) sleep (21.0 min). Fitbit Charge 4 and Garmin Vivosmart 4 both showed moderate accuracy in assessing sleep stages and total sleep time compared to PSG. Fitbit Charge 4 showed the least disagreement for REM sleep (4.0 min) relative to PSG. Additionally, Fitbit Charge 4 showed higher sensitivities to deep sleep (75%) and REM sleep (86.5%) compared to Garmin Vivosmart 4 and WHOOP. Conclusions: The findings of this systematic literature review indicate that the devices with higher relative agreement and sensitivities to multistate sleep (ie, Fitbit Charge 4 and WHOOP) seem appropriate for deriving suitable estimates of sleep parameters. However, analyses regarding the multistate categorization of sleep indicate that all devices can benefit from further improvement in the assessment of specific sleep stages. Although providers are continuously developing new versions and variants of wearables, the scientific research on these wearables remains considerably limited. This scarcity in literature not only reduces our ability to draw definitive conclusions but also highlights the need for more targeted research in this domain. Additionally, future research endeavors should strive for standardized protocols including larger sample sizes to enhance the comparability and power of the results across studies.

The relationship between manual ability, dystonia and choreoathetosis severity and upper limb movement patterns during reaching and grasping in children and young adults with dyskinetic cerebral palsy.

INTRODUCTION: Impaired upper limb movements are a key feature in dyskinetic cerebral palsy (CP). However, information on how specific movement patterns relate to manual ability, performance and underlying movement disorders is lacking. Insight in these associations may contribute to targeted upper limb management in dyskinetic CP. This study aimed to explore associations between deviant upper limb movement patterns and (1) manual ability, (2) severity of dystonia/choreoathetosis, and (3) movement time/trajectory deviation during reaching and grasping. PARTICIPANTS/METHODS: Participants underwent three-dimensional upper limb analysis during reaching forwards (RF), reaching sideways (RS) and reach-and-grasp vertical (RGV) as well as clinical assessment. Canonical correlation and regression analysis with statistical parametric mapping were used to explore associations between clinical/performance parameters and movement patterns (mean and variability). RESULTS: Thirty individuals with dyskinetic CP participated (mean age 16±5 y; 20 girls). Lower manual ability was related to higher variability in wrist flexion/extension during RF and RS early in the reaching cycle (p < 0.05). Higher dystonia severity was associated with higher mean wrist flexion (40-82 % of the reaching cycle; p = 0.004) and higher variability in wrist flexion/extension (31-75 %; p < 0.001) and deviation (2-14 %; p = 0.007/60-73 %; p = 0.006) during RF. Choreoathetosis severity was associated with higher elbow pro/supination variability (12-19 %; p = 0.009) during RGV. Trajectory deviation was associated with wrist and elbow movement variability (p < 0.05). CONCLUSION: Current novel analysis of upper limb movement patterns and respective timings allows to detect joint angles and periods in the movement cycle wherein associations with clinical parameters occur. These associations are not present at each joint level, nor during the full movement cycle. This knowledge should be considered for individualized treatment strategies.

A Novel Video-Based Methodology for Automated Classification of Dystonia and Choreoathetosis in Dyskinetic Cerebral Palsy During a Lower Extremity Task.

BACKGROUND: Movement disorders in children and adolescents with dyskinetic cerebral palsy (CP) are commonly assessed from video recordings, however scoring is time-consuming and expert knowledge is required for an appropriate assessment. OBJECTIVE: To explore a machine learning approach for automated classification of amplitude and duration of distal leg dystonia and choreoathetosis within short video sequences. METHODS: Available videos of a heel-toe tapping task were preprocessed to optimize key point extraction using markerless motion analysis. Postprocessed key point data were passed to a time series classification ensemble algorithm to classify dystonia and choreoathetosis duration and amplitude classes (scores 0, 1, 2, 3, and 4), respectively. As ground truth clinical scoring of dystonia and choreoathetosis by the Dyskinesia Impairment Scale was used. Multiclass performance metrics as well as metrics for summarized scores: absence (score 0) and presence (score 1-4) were determined. RESULTS: Thirty-three participants were included: 29 with dyskinetic CP and 4 typically developing, age 14 years:6 months ± 5 years:15 months. The multiclass accuracy results for dystonia were 77% for duration and 68% for amplitude; for choreoathetosis 30% for duration and 38% for amplitude. The metrics for score 0 versus score 1 to 4 revealed an accuracy of 81% for dystonia duration, 77% for dystonia amplitude, 53% for choreoathetosis duration and amplitude. CONCLUSIONS: This methodology study yielded encouraging results in distinguishing between presence and absence of dystonia, but not for choreoathetosis. A larger dataset is required for models to accurately represent distinct classes/scores. This study presents a novel methodology of automated assessment of movement disorders solely from video data.

Altered upper limb kinematics in individuals with dyskinetic cerebral palsy in comparison with typically developing peers - A statistical parametric mapping study.

BACKGROUND: Dyskinetic cerebral palsy (DCP) is clinically characterized by involuntary movements and abnormal postures, which can aggravate with activity. While upper limb movement variability is often detected in the clinical picture, it remains unknown how movement patterns of individuals with DCP differ from typically developing (TD) peers. RESEARCH QUESTION: Do individuals with DCP show i) higher time-dependent standard deviations of upper limb joint angles and ii) altered upper limb kinematics in time and/or amplitude during functional upper limb tasks in comparison with TD individuals? METHODS: Three-dimensional upper limb movement patterns were cross-sectionally compared in 50 individuals with and without DCP during three functional tasks: reach forward (RF), reach and grasp vertical (RGV) and reach sideways (RS). Mean and point-wise standard deviations of angular waveform of the upper limb joint angles were compared between groups to evaluate differences in time and/or amplitude using traditional and non-linear registration statistical parametric mapping. RESULTS: Thirty-five extremities from 30 individuals (mean age 17y4m, range 5-25 y; MACS level I(n = 2); II(n = 15); III(n = 16); IV(n = 2)) with DCP and twenty TD individuals (mean age 16y8m, range 8-25 y) were evaluated. The DCP compared to TD group showed higher point-wise standard deviations at the level of all joints, which was time-dependent and varied between tasks. Mean wrist and elbow flexion was higher for the DCP group during RF (0-83 % wrist; 57-100 % elbow), RGV (0-82 % wrist; 12-100 % elbow) and RS (0-43 % wrist; 70-100 % elbow). SIGNIFICANCE: This is the first study exploring the movement patterns of individuals with DCP during reaching using quantitative measures. Analyzing these individual movement patterns by statistical parametric mapping (SPM) allows us to focus on both specific joint or on specific timing during the movement cycle. The individual information that this method yields can guide individual therapy aiming to improve reaching function in different parts of the movement cycle or evaluate intervention effects on upper extremity treatment.

System identification of mGluR-dependent long-term depression.

Recent advances have started to uncover the underlying mechanisms of metabotropic glutamate receptor (mGluR)-dependent long-term depression (LTD). However, it is not completely clear how these mechanisms are linked, and it is believed that several crucial mechanisms remain to be revealed. In this study, we investigated whether system identification (SI) methods can be used to gain insight into the mechanisms of synaptic plasticity. SI methods have been shown to be an objective and powerful approach for describing how sensory neurons encode information about stimuli. However, to our knowledge, it is the first time that SI methods have been applied to electrophysiological brain slice recordings of synaptic plasticity responses. The results indicate that the SI approach is a valuable tool for reverse-engineering of mGluR-LTD responses. We suggest that such SI methods can aid in unraveling the complexities of synaptic function.