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ABSTRACT: Connolly, DR, Stolp, S, Gualtieri, A, Ferrari Bravo, D, Sassi, R, Rampinini, E, and Coutts, AJ. How do young soccer players train? A 5-year analysis of weekly training load and its variability between age groups in an elite youth academy. J Strength Cond Res 38(8): e423-e429, 2024-The aim of this study was to quantify the session rating of perceived exertion (sRPE), duration, and training load accrued across typical training weeks undertaken by youth soccer players. Differences between starters, nonstarters, and variations in training load variables were also investigated. Data were collected from 230 elite youth players in 4 age groups (U15, U16, U17, and U19) during 5 competitive seasons. Mixed models were used to describe variation between age groups and compare starters with nonstarters, with season as a fixed covariate effect. Week-to-week variation in training load was expressed as the percentage coefficient of variation. The main findings may be used to highlight a significant effect of age and playing status on training intensity, duration, and internal training load. Weekly training load increased progressively from the U15 to U17, with significant differences between each age group (p < 0.03). Lower mean weekly perceived intensity (sRPE) was noted in U15 when compared with the older age groups (4.2 vs. 4.6-4.9 arbitrary unit for U16 to U19, p < 0.001). Low weekly training load variation was observed across the different phases of the season in each age group, with the preseason exhibiting the greatest variance (3.6-6.2%). Differences in the training load are likely more attributable to changes in training duration rather than sRPE. Control of session duration seems to play an important role when aiming to control load in the academy environment, and practitioners should closely monitor the differences in duration and load being recorded between starters and nonstarters.
Subject(s)
Physical Conditioning, Human , Soccer , Humans , Soccer/physiology , Adolescent , Physical Conditioning, Human/physiology , Physical Conditioning, Human/methods , Age Factors , Male , Physical Exertion/physiology , Athletic Performance/physiology , Child , AthletesABSTRACT
Atrial fibrillation (AF), the most prevalent cardiac rhythm disorder, significantly increases hospitalization and health risks. Reverting from AF to sinus rhythm (SR) often requires intensive interventions. This study presents a deep-learning model capable of predicting the transition from SR to AF on average 30.8 min before the onset appears, with an accuracy of 83% and an F1 score of 85% on the test data. This performance was obtained from R-to-R interval signals, which can be accessible from wearable technology. Our model, entitled Warning of Atrial Fibrillation (WARN), consists of a deep convolutional neural network trained and validated on 24-h Holter electrocardiogram data from 280 patients, with 70 additional patients used for testing and further evaluation on 33 patients from two external centers. The low computational cost of WARN makes it ideal for integration into wearable technology, allowing for continuous heart monitoring and early AF detection, which can potentially reduce emergency interventions and improve patient outcomes.
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BACKGROUND: Nutrition exerts a fundamental role in the prevention of obesity (OB). The aim of this study was to assess the extent to which well recognized risk factors for early OB can be associated to overweight (OW) or OB under a standardized nutritional approach and surveillance in toddlers. METHODS: The eligible population was represented by 676 toddlers aged 24-36 months, assigned to 18 primary care pediatricians trained on nutritional issues who shared a standardized nutritional approach. Six-hundred-twenty-nine children (333 boys), mean age 27.8 ± 4.2 months were effectively included in this observational study. Parents received nutritional advice with particular emphasis to proteins and sugar composition supported by leaflets and reinforced at each visit. Body mass index was assessed at the age of 24-36 months. The following individual and family risk factors were considered: gestational age, birth weight, eutocic/caesarean delivery, milk feeding history, household smoking or antibiotics exposure, parents' weight, height and educational level. Prevalence of OW/OB was compared to a group of 742 toddlers (373 boys) under usual care. RESULTS: Under a standardized nutritional counselling, 28.1% toddlers were classified as OW/OB compared to 36.9% toddlers under usual care (p = 0.005). In unadjusted models, parental OW/OB was significantly associated to OW/OB in toddlers (p < 0.01), while high birth weight did not reach statistical significance (p = 0.07). In adjusted models, including all the explanatory variables studied, only paternal OW/OB vs. normal weight was significantly associated to OW/OB in toddlers (OR 2.035, 95% confidence interval 1.206-3.436). No protective effect of exclusive breast feeding during the first 6 months of age was demonstrated. CONCLUSIONS: Toddlers under a standardized nutrition counselling focused to limit protein and simple sugars, showed lower prevalence of OW/OB compared to usual care. Healthy promotion activities should take into account the influence of paternal BMI on the offspring adiposity.
Subject(s)
Pediatric Obesity , Humans , Male , Female , Risk Factors , Pediatric Obesity/prevention & control , Pediatric Obesity/epidemiology , Child, Preschool , Body Mass Index , Counseling , Prevalence , Italy/epidemiologyABSTRACT
BACKGROUND: The proportion of Canadian youth seeking mental health support from an emergency department (ED) has risen in recent years. As EDs typically address urgent mental health crises, revisiting an ED may represent unmet mental health needs. Accurate ED revisit prediction could aid early intervention and ensure efficient healthcare resource allocation. We examine the potential increased accuracy and performance of graph neural network (GNN) machine learning models compared to recurrent neural network (RNN), and baseline conventional machine learning and regression models for predicting ED revisit in electronic health record (EHR) data. METHODS: This study used EHR data for children and youth aged 4-17 seeking services at McMaster Children's Hospital's Child and Youth Mental Health Program outpatient service to develop and evaluate GNN and RNN models to predict whether a child/youth with an ED visit had an ED revisit within 30 days. GNN and RNN models were developed and compared against conventional baseline models. Model performance for GNN, RNN, XGBoost, decision tree and logistic regression models was evaluated using F1 scores. RESULTS: The GNN model outperformed the RNN model by an F1-score increase of 0.0511 and the best performing conventional machine learning model by an F1-score increase of 0.0470. Precision, recall, receiver operating characteristic (ROC) curves, and positive and negative predictive values showed that the GNN model performed the best, and the RNN model performed similarly to the XGBoost model. Performance increases were most noticeable for recall and negative predictive value than for precision and positive predictive value. CONCLUSIONS: This study demonstrates the improved accuracy and potential utility of GNN models in predicting ED revisits among children and youth, although model performance may not be sufficient for clinical implementation. Given the improvements in recall and negative predictive value, GNN models should be further explored to develop algorithms that can inform clinical decision-making in ways that facilitate targeted interventions, optimize resource allocation, and improve outcomes for children and youth.
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Deep Learning , Hospitalization , Child , Humans , Adolescent , Outpatients , Mental Health , Canada , Emergency Service, HospitalABSTRACT
In utero, the developing brain is highly susceptible to the environment. For example, adverse maternal experiences during the prenatal period are associated with outcomes such as altered neurodevelopment and emotion dysregulation. Yet, the underlying biological mechanisms remain unclear. Here, we investigate whether the function of a network of genes co-expressed with the serotonin transporter in the amygdala moderates the impact of prenatal maternal adversity on the structure of the orbitofrontal cortex (OFC) in middle childhood and/or the degree of temperamental inhibition exhibited in toddlerhood. T1-weighted structural MRI scans were acquired from children aged 6-12 years. A cumulative maternal adversity score was used to conceptualize prenatal adversity and a co-expression based polygenic risk score (ePRS) was generated. Behavioural inhibition at 18 months was assessed using the Early Childhood Behaviour Questionnaire (ECBQ). Our results indicate that in the presence of a low functioning serotonin transporter gene network in the amygdala, higher levels of prenatal adversity are associated with greater right OFC thickness at 6-12 years old. The interaction also predicts temperamental inhibition at 18 months. Ultimately, we identified important biological processes and structural modifications that may underlie the link between early adversity and future deviations in cognitive, behavioural, and emotional development.
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Gene Regulatory Networks , Serotonin Plasma Membrane Transport Proteins , Female , Pregnancy , Humans , Child , Child, Preschool , Serotonin Plasma Membrane Transport Proteins/genetics , Prefrontal Cortex/diagnostic imaging , FamilyABSTRACT
AI techniques have recently been put under the spotlight for analyzing electrocardiograms (ECGs). However, the performance of AI-based models relies on the accumulation of large-scale labeled datasets, which is challenging. To increase the performance of AI-based models, data augmentation (DA) strategies have been developed recently. The study presented a comprehensive systematic literature review of DA for ECG signals. We conducted a systematic search and categorized the selected documents by AI application, number of leads involved, DA method, classifier, performance improvements after DA, and datasets employed. With such information, this study provided a better understanding of the potential of ECG augmentation in enhancing the performance of AI-based ECG applications. This study adhered to the rigorous PRISMA guidelines for systematic reviews. To ensure comprehensive coverage, publications between 2013 and 2023 were searched across multiple databases, including IEEE Explore, PubMed, and Web of Science. The records were meticulously reviewed to determine their relevance to the study's objective, and those that met the inclusion criteria were selected for further analysis. Consequently, 119 papers were deemed relevant for further review. Overall, this study shed light on the potential of DA to advance the field of ECG diagnosis and monitoring.
Subject(s)
Artificial Intelligence , Electrocardiography , Databases, Factual , PubMedABSTRACT
Dysregulation is a combination of emotion, behavior, and attention problems associated with lifelong psychiatric comorbidity. There is evidence for the stability of dysregulation from childhood to adulthood, which would be more fully characterized by determining the likely stability from infancy to childhood. Early origins of dysregulation can further be validated and contextualized in association with environmental and biological factors, such as prenatal stress and polygenic risk scores (PRS) for overlapping child psychiatric problems. We aimed to determine trajectories of dysregulation from 3 months to 5 years (N = 582) in association with maternal prenatal depression moderated by multiple child PRS (N = 232 pairs with available PRS data) in a prenatal cohort. Mothers reported depression symptoms at 24-26 weeks' gestation and child dysregulation at 3, 6, 18, 36, 48, and 60 months. The PRS were for major depressive disorder, attention deficit hyperactivity disorder, cross disorder, and childhood psychiatric problems. Covariates were biological sex, maternal education, and postnatal depression. Analyses included latent classes and regression. Two dysregulation trajectories emerged: persistently low dysregulation (94%), and increasingly high dysregulation (6%). Stable dysregulation emerged at 18 months. High dysregulation was associated with maternal prenatal depression, moderated by PRS for child comorbid psychiatric problems. Males were at greater risk of high dysregulation.
Subject(s)
Depression, Postpartum , Depressive Disorder, Major , Female , Humans , Male , Pregnancy , Comorbidity , Depression/epidemiology , Depression/genetics , Depression/psychology , Depression, Postpartum/psychology , Depressive Disorder, Major/epidemiology , Depressive Disorder, Major/genetics , Mothers/psychology , Infant , Child, PreschoolABSTRACT
Introduction: Environmental perturbations during critical periods can have pervasive, organizational effects on neurodevelopment. To date, the literature examining the long-term impact of early life adversity has largely investigated structural and functional imaging data outcomes independently. However, emerging research points to a relationship between functional connectivity and the brain's underlying structural architecture. For instance, functional connectivity can be mediated by the presence of direct or indirect anatomical pathways. Such evidence warrants the use of structural and functional imaging in tandem to study network maturation. Accordingly, this study examines the impact of poor maternal mental health and socioeconomic context during the perinatal period on network connectivity in middle childhood using an anatomically weighted functional connectivity (awFC) approach. awFC is a statistical model that identifies neural networks by incorporating information from both structural and functional imaging data. Methods: Resting-state fMRI and DTI scans were acquired from children aged 7-9 years old. Results: Our results indicate that maternal adversity during the perinatal period can affect offspring's resting-state network connectivity during middle childhood. Specifically, in comparison to controls, children of mothers who had poor perinatal maternal mental health and/or low socioeconomic status exhibited greater awFC in the ventral attention network. Discussion: These group differences were discussed in terms of the role this network plays in attention processing and maturational changes that may accompany the consolidation of a more adult-like functional cortical organization. Furthermore, our results suggest that there is value in using an awFC approach as it may be more sensitive in highlighting connectivity differences in developmental networks associated with higher-order cognitive and emotional processing, as compared to stand-alone FC or SC analyses.
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BACKGROUND AND OBJECTIVE: Planning the optimal ablation strategy for the treatment of complex atrial tachycardia (CAT) is a time consuming task and is error-prone. Recently, directed network mapping, a technology based on graph theory, proved to efficiently identify CAT based solely on data of clinical interventions. Briefly, a directed network was used to model the atrial electrical propagation and reentrant activities were identified by looking for closed-loop paths in the network. In this study, we propose a recommender system, built as an optimization problem, able to suggest the optimal ablation strategy for the treatment of CAT. METHODS: The optimization problem modeled the optimal ablation strategy as that one interrupting all reentrant mechanisms while minimizing the ablated atrial surface. The problem was designed on top of directed network mapping. Considering the exponential complexity of finding the optimal solution of the problem, we introduced a heuristic algorithm with polynomial complexity. The proposed algorithm was applied to the data of i) 6 simulated scenarios including both left and right atrial flutter; and ii) 10 subjects that underwent a clinical routine. RESULTS: The recommender system suggested the optimal strategy in 4 out of 6 simulated scenarios. On clinical data, the recommended ablation lines were found satisfactory on 67% of the cases according to the clinician's opinion, while they were correctly located in 89%. The algorithm made use of only data collected during mapping and was able to process them nearly real-time. CONCLUSIONS: The first recommender system for the identification of the optimal ablation lines for CAT, based solely on the data collected during the intervention, is presented. The study may open up interesting scenarios for the application of graph theory for the treatment of CAT.
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Atrial Flutter , Catheter Ablation , Tachycardia, Supraventricular , Humans , Atrial Flutter/surgery , Heart Atria/surgery , Treatment OutcomeABSTRACT
Neuroimaging-based brain age is a biomarker that is generated by machine learning (ML) predictions. The brain age gap (BAG) is typically defined as the difference between the predicted brain age and chronological age. Studies have consistently reported a positive BAG in individuals with schizophrenia (SCZ). However, there is little understanding of which specific factors drive the ML-based brain age predictions, leading to limited biological interpretations of the BAG. We gathered data from three publicly available databases - COBRE, MCIC, and UCLA - and an additional dataset (TOPSY) of early-stage schizophrenia (82.5% untreated first-episode sample) and calculated brain age with pre-trained gradient-boosted trees. Then, we applied SHapley Additive Explanations (SHAP) to identify which brain features influence brain age predictions. We investigated the interaction between the SHAP score for each feature and group as a function of the BAG. These analyses identified total gray matter volume (group × SHAP interaction term ß = 1.71 [0.53; 3.23]; pcorr < 0.03) as the feature that influences the BAG observed in SCZ among the brain features that are most predictive of brain age. Other brain features also presented differences in SHAP values between SCZ and HC, but they were not significantly associated with the BAG. We compared the findings with a non-psychotic depression dataset (CAN-BIND), where the interaction was not significant. This study has important implications for the understanding of brain age prediction models and the BAG in SCZ and, potentially, in other psychiatric disorders.
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Mood and anxiety disorders typically begin in adolescence and have overlapping clinical features but marked inter-individual variation in clinical presentation. The use of multimodal neuroimaging data may offer novel insights into the underlying brain mechanisms. We applied Heterogeneity Through Discriminative Analysis (HYDRA) to measures of regional brain morphometry, neurite density, and intracortical myelination to identify subtypes of youth, aged 9-10 years, with mood and anxiety disorders (N = 1931) compared to typically developing youth (N = 2823). We identified three subtypes that were robust to permutation testing and sample composition. Subtype 1 evidenced a pattern of imbalanced cortical-subcortical maturation compared to the typically developing group, with subcortical regions lagging behind prefrontal cortical thinning and myelination and greater cortical surface expansion globally. Subtype 2 displayed a pattern of delayed cortical maturation indicated by higher cortical thickness and lower cortical surface area expansion and myelination compared to the typically developing group. Subtype 3 showed evidence of atypical brain maturation involving globally lower cortical thickness and surface coupled with higher myelination and neural density. Subtype 1 had superior cognitive function in contrast to the other two subtypes that underperformed compared to the typically developing group. Higher levels of parental psychopathology, family conflict, and social adversity were common to all subtypes, with subtype 3 having the highest burden of adverse exposures. These analyses comprehensively characterize pre-adolescent mood and anxiety disorders, the biopsychosocial context in which they arise, and lay the foundation for the examination of the longitudinal evolution of the subtypes identified as the study sample transitions through adolescence.
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Anxiety Disorders , Brain , Humans , Adolescent , Neuroimaging/methods , Psychopathology , Affect , Magnetic Resonance ImagingABSTRACT
Negative emotionality (NE) was evaluated as a candidate mechanism linking prenatal maternal affective symptoms and offspring internalizing problems during the preschool/early school age period. The participants were 335 mother-infant dyads from the Maternal Adversity, Vulnerability and Neurodevelopment project. A Confirmatory Bifactor Analysis (CFA) based on self-report measures of prenatal depression and pregnancy-specific anxiety generated a general factor representing overlapping symptoms of prenatal maternal psychopathology and four distinct symptom factors representing pregnancy-specific anxiety, negative affect, anhedonia and somatization. NE was rated by the mother at 18 and 36 months. CFA based on measures of father, mother, child-rated measures and a semistructured interview generated a general internalizing factor representing overlapping symptoms of child internalizing psychopathology accounting for the unique contribution of each informant. Path analyses revealed significant relationships among the general maternal affective psychopathology, the pregnancy- specific anxiety, and the child internalizing factors. Child NE mediated only the relationship between pregnancy-specific anxiety and the child internalizing factors. We highlighted the conditions in which prenatal maternal affective symptoms predicts child internalizing problems emerging early in development, including consideration of different mechanistic pathways for different maternal prenatal symptom presentations and child temperament.
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Affect , Depression , Female , Infant , Pregnancy , Child , Humans , Child, Preschool , Depression/psychology , Anxiety/psychology , Mothers/psychology , Child Behavior/psychologyABSTRACT
Determining the potential cardiotoxicity and pro-arrhythmic effects of drug candidates remains one of the most relevant issues in the drug development pipeline (DDP). New methods enabling to perform more representative preclinical in vitro studies by exploiting induced pluripotent stem cell-derived cardiomyocytes (iPSC-CM) are under investigation to increase the translational power of the outcomes. Here we present a pharmacological campaign conducted to evaluate the drug-induced QT alterations and arrhythmic events on uHeart, a 3D miniaturized in vitro model of human myocardium encompassing iPSC-CM and dermal fibroblasts embedded in fibrin. uHeart was mechanically trained resulting in synchronously beating cardiac microtissues in 1 week, characterized by a clear field potential (FP) signal that was recorded by means of an integrated electrical system. A drug screening protocol compliant with the new International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) guidelines was established and uHeart was employed for testing the effect of 11 compounds acting on single or multiple cardiac ion channels and well-known to elicit QT prolongation or arrhythmic events in clinics. The alterations of uHeart's electrophysiological parameters such as the beating period, the FP duration, the FP amplitude, and the detection of arrhythmic events prior and after drug administration at incremental doses were effectively analyzed through a custom-developed algorithm. Results demonstrated the ability of uHeart to successfully anticipate clinical outcome and to predict the QT prolongation with a sensitivity of 83.3%, a specificity of 100% and an accuracy of 91.6%. Cardiotoxic concentrations of drugs were notably detected in the range of the clinical highest blood drug concentration (Cmax), qualifying uHeart as a fit-to-purpose preclinical tool for cardiotoxicity studies.
Subject(s)
Drug Evaluation, Preclinical , Induced Pluripotent Stem Cells , Lab-On-A-Chip Devices , Long QT Syndrome , Humans , Cardiotoxicity , Drug Evaluation, Preclinical/methods , Ion Channels , Long QT Syndrome/chemically induced , Myocytes, Cardiac , Pharmaceutical PreparationsABSTRACT
Background: Secure attachment reflects caregiver-child relationship in which the caregiver is responsive when support and comforting are needed by the child. This pattern of bond has an important buffering role in the response to stress by the reduction of the negative experience and its associated physiological response. Disruption of the physiological stress system is thought to be a central mechanism by which early care impacts children. Early life stress causes cellular and molecular changes in brain regions associated with cognitive functions that are fundamental for early learning. Methods: The association between attachment, cortisol response before and after the Strange Situation Experiment, and neurodevelopment was examined in a sample of 107 preschoolers at age three. Also, the predictive effect of cortisol reactivity and attachment on telomere length at age seven was investigated in a followed-up sample of 77 children. Results: Children with insecure attachment had higher cortisol secretion and poorer neurodevelopmental skills at age three. A significant cortisol change was observed across the experiment with non-significant interaction with attachment. The attachment and neurodevelopment association was not mediated by cortisol secretion. Preschoolers' attachment and cortisol did not associate nor interacted to predict telomere length at age seven. Conclusion: These findings add evidence to the detrimental effects of insecure attachment as an aggravator of the physiological response to stress and poorer neurodevelopment during the preschool period. Although attachment and cortisol were not predictive of telomere length, intervention policies that promote secure attachment are more likely to positively echo on several health domains.
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The hypothalamus is a small grey matter structure which plays a crucial role in many physiological functions. Some studies have found an association between hypothalamic volume and psychopathology, which stresses the need for a standardized method to maximize segmentation accuracy. Here, we provide a detailed step-by-step method outlining the procedures to manually segment the hypothalamus using anatomical T1w images from 3T scanners, which many neuroimaging studies collect as a standard anatomical reference image. We compared volumes generated by manual segmentation and those generated by an automatic algorithm, observing a significant difference between automatically and manually segmented hypothalamus volumes on both sides (left: U = 222842, p-value < 2.2e-16; right: U = 218520, p- value < 2.2e-16).â¢Significant difference exists between existing automatic segmentation methods and the manual segmentation procedure.â¢We discuss potential drift effects, segmentation quality issues, and suggestions on how to mitigate them.â¢We demonstrate that the present manual segmentation procedure using standard T1-weighted MRI may be significantly more accurate than automatic segmentation outputs.
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OBJECTIVE: T-wave alternans (TWA) manifests as beat-to-beat fluctuations of T-wave morphology on the electrocardiogram (ECG), with physiological bases not fully understood. Using a biophysical model of the ECG, we demonstrate and give explicit relations that TWA depends on the i) spatial covariance between myocytes' repolarization time and alternans; and ii) global alternans (common to every myocyte). METHODS: We quantified the spatial covariance and global alternans by means of two new metrics, R index and δ, respectively. They were validated on both synthetic and real signals. Computerized simulations were generated using a biophysical model linking the action potentials with the surface ECG. Then, the metrics were computed in STAFF-III dataset, containing ECGs from patients who underwent coronary angioplasty with prolonged balloon inflations, and the time courses of the metrics were analyzed together with TWA measured on the surface ECG. RESULTS: The metrics properly estimated the spatial covariance and global alternans in the synthetic data. In the STAFF-III dataset, the R index progressively increased from baseline to the fourth minute of inflation (median ∆R=0.81 ms; p 0.05), whereas δ was mostly unaltered during the intervention ( δ=0 ms). CONCLUSION: We reported, for the first time, that TWA is significantly driven by the myocyte's spatial covariance between their repolarization times and alternans, and not by global alternans, when TWA is generated by regional ischemia. SIGNIFICANCE: The metrics may reveal new complementary insights into the mechanisms underlying TWA.
Subject(s)
Arrhythmias, Cardiac , Electrocardiography , Humans , Arrhythmias, Cardiac/diagnosis , Action Potentials , Muscle CellsABSTRACT
The dearth of child and adolescent mental health services (CAMHS) is a global problem. Integrating CAMHS in primary care has been offered as a solution. We sampled integrated care perspectives from colleagues around the world. Our findings include various models of integrated care namely: the stepped care model in Australia; shared care in the United Kingdom (UK) and Spain; school-based collaborative care in Qatar, Singapore and the state of Texas in the US; collaborative care in Canada, Brazil, US, and Uruguay; coordinated care in the US; and, developing collaborative care models in low-resource settings, like Kenya and Micronesia. These findings provide insights into training initiatives necessary to build CAMHS workforce capacity using integrated care models, each with the ultimate goal of improving access to care. Despite variations and progress in implementing integrated care models internationally, common challenges exist: funding within complex healthcare systems, limited training mechanisms, and geopolitical/policy issues. Supportive healthcare policy, robust training initiatives, ongoing quality improvement and measurement of outcomes across programs would provide data-driven support for the expansion of integrated care and ensure its sustainability.