BACKGROUND: This study examined the strength, shape and direction of associations of accelerometer-assessed overall, school- and non-school-based moderate-to-vigorous physical activity (MVPA) and sedentary time (ST) with BMI among adolescents across the world. Second, we examined whether these associations differed by study site and sex. METHODS: Cross-sectional data from the IPEN Adolescent study, an observational multi-country study, were used. Participants wore an accelerometer for seven days, reported height and weight, and completed a socio-demographic survey. In total, 4852 adolescents (46.6% boys), aged 11-19 years (mean age = 14.6, SD = 1.7 years) were included in the analyses, using generalized additive mixed models. RESULTS: Adolescents accumulated on average 41.3 (SD = 22.6) min/day of MVPA and 531.8 (SD = 81.1) min/day of ST, and the prevalence of overweight and obesity was 17.2% (IOTF), but these mean values differed by country. Linear negative associations of accelerometer-based MVPA and ST with standardized BMI scores and the likelihood of being overweight/obese were found. School-based ST and non-school-based MVPA were more strongly negatively associated to the outcomes than non-school based ST and school-based MVPA. Study site moderated the associations; adolescent sex did not. No curvilinear associations were found. CONCLUSIONS: This multi-country study confirmed the importance of MVPA as a potential protective factor against overweight/obesity in adolescents. Non-school-based MVPA seemed to be the main driver of these associations. Unexpected results were found for ST, calling for further examination in methodologically sound international studies but using inclinometers or pressure sensors to provide more precise ST measures.
- MeSH
- Accelerometry MeSH
- Exercise MeSH
- Body Mass Index MeSH
- Humans MeSH
- Adolescent MeSH
- Overweight * epidemiology prevention & control MeSH
- Obesity epidemiology prevention & control MeSH
- Cross-Sectional Studies MeSH
- Sedentary Behavior * MeSH
- Check Tag
- Humans MeSH
- Adolescent MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Observational Study MeSH
- Research Support, Non-U.S. Gov't MeSH
- Research Support, N.I.H., Extramural MeSH
The multitude of training models and curricula for the specialty of clinical neuropsychology around the world has led to organized activities to develop a framework of core competencies to ensure sufficient expertise among entry-level professionals in the field. The Standing Committee on Clinical Neuropsychology of the European Federation of Psychologists' Associations is currently working toward developing a specialty certification in clinical neuropsychology to establish a cross-national standard against which to measure levels of equivalency and uniformity in competence and service provision among professionals in the field. Through structured interviews with experts from 28 European countries, we explored potential areas of core competency. Specifically, questions pertained to the perceived importance of a series of foundational, functional, and other competencies, as well as current training standards and practices, and optimal standards. Our findings revealed considerable agreement (about three quarters and above) on academic and clinical training, despite varied actual training requirements currently, with fewer respondents relegating importance to training in teaching, supervision, and research (a little over half), and even fewer to skills related to management, administration, and advocacy (fewer than half). European expert clinical neuropsychologists were in agreement with previous studies (including those conducted in the United States, Australia, and other countries) regarding the importance of sound theoretical and clinical training but management, administrative, and advocacy skills were not central to their perspective of a competent specialist in clinical neuropsychology. Establishing a specialty certificate in clinical neuropsychology based on core competencies may enable mobility of clinical neuropsychologists across Europe, and, perhaps, provide an impetus for countries with limited criteria to reconsider their training requirements and harmonize their standards with others.
- Publication type
- Journal Article MeSH
INTRODUCTION: Social emotional competence is fundamental to the positive development of children and youth. Accurately understanding and assessing children's social emotional competencies, using psychometrically sound instruments, are essential to global efforts to support children's social emotional learning, academic achievements, and health. This study examined the psychometric properties of a teacher-reported measure of young children's social emotional competence, the Social Competence Scale - Teacher edition (SCS-T), in two samples of children growing up with varied economic resources/conditions, cultural norms, and educational experiences, namely Pakistan (N = 396) and Sweden (N = 309). METHODS: Participants were aged 4-6 years old. The study design was cross-sectional. RESULTS AND DISCUSSION: Using structural equation modelling, bi-factor confirmatory factor analysis models implying shared variance, among all items and domain-specific shared variance, among the prosocial items, emotion regulation items, and academic skills items resulted in good fitting models in each respective sample. Invariance testing across samples revealed a subset of items from each factor structure with partial scalar invariance, whereby five items had equal thresholds and could be comparable across the two samples. Thus, results provided partial support for hypotheses 1, 2, and 3, in that the posited three factor model (H1) was not clearly supported and a bi-factor model evidenced the best fit, among tested models, for both samples. Further, partial scalar invariance (H3) was found for five items out of 25 items, concerning social competence and academic skills. In regards, to the posited research question, the results of Z-tests showed significant (p < 0.001) latent mean differences between the samples. Compared to the Swedish sample, the Pakistani sample was 1.80 units lower on social competence (z = -6.41, p < 0.001) and 1.86 units lower on academic skills (z = -7.87, p < 0.001). The implications of these findings in light of efforts to promote positive child development in diverse parts of the world are considered.
- Publication type
- Journal Article MeSH
Background and Objectives Automatic detection of breathing disorders plays an important role in the early signalization of respiratory diseases. Measuring methods can be based on electrocardiogram (ECG), sound, oximetry, or respiratory analysis. However, these approaches require devices placed on the human body or they are prone to disturbance by environmental influences. To solve these problems, we proposed a heart contraction mechanical trigger for unobtrusive detection of respiratory disorders from the mechanical measurement of cardiac contractions. We designed a novel method to calculate this mechanical trigger purely from measured mechanical signals without the use of ECG. Methods The approach is a built-on calculation of the so-called euclidean arc length from the signals. In comparison to previous researches, this system does not require any equipment attached to a person. This is achieved by locating the tensometers on the bed. Data from sensors are fused by the Cartan curvatures method to beat-to-beat vector input for the Convolutional neural network (CNN) classifier. Results In sum, 2281 disordered and 5130 normal breathing samples was collected for analysis. The experiments with use of 10-fold cross validation show that accuracy, sensitivity, and specificity reach values of 96.37%, 92.46%, and 98.11% respectively. Conclusions By the approach for detection, the system offers a novel way for a completely unobtrusive diagnosis of breathing-related health problems. The proposed solution can effectively be deployed in all clinical or home environments.
- MeSH
- Algorithms MeSH
- Electrocardiography * MeSH
- Humans MeSH
- Respiratory Tract Diseases * MeSH
- Neural Networks, Computer MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
BACKGROUND: Prenatal origins of wheezing are not fully understood. This study develops a model of mechanisms linking perinatal stress exposure to wheeze phenotypes in children. METHODS: Data were obtained from 1880 mother-child dyads participating in ELSPAC-CZ birth cohort. Wheeze phenotypes assessed between birth and age 7 years included "never wheeze," "early-onset transient (EOT) wheeze," "early-onset persistent (EOP) wheeze," and "late-onset (LO) wheeze." Prenatal and postnatal stress exposures were assessed in mid-pregnancy and 6 months after delivery, respectively, using an inventory of 42 life events. RESULTS: In adjusted models, children in the highest tercile (high) versus lowest tercile (low) for prenatal life events had a 38% higher risk of EOT wheeze (relative risk ratio [RRR] = 1.38; 95% confidence interval [CI] = 1.01-1.88; p = .041) and 50% higher risk of LO wheeze (RRR = 1.50; 95% CI = 1.00-2.25; p = .047). High versus low exposure to postnatal life events predicted a 60% increase in relative risk of EOT wheeze (RRR = 1.60; 95% CI = 1.17-2.19; p = .003) and medium versus low exposure was related to an 85% increase in relative risk of EOP wheeze (RRR = 1.85; 95% CI = 1.16-2.95; p = .010). Lower respiratory tract infections and postpartum depression partially mediated between postnatal life events and any wheeze (indirect effects 1.06, 95% CI = 1.02-1.09, p = .003 and odds ratio [OR] = 1.08, 95% CI = 1.02-1.15, p = .012, respectively), while postnatal events mediate for prenatal events (indirect effect OR = 1.11; 95% CI = 1.03-1.18; p = .005). CONCLUSIONS: Exposures to prenatal and postnatal life events are risk factors for the development of wheezing. Prenatal stress contributes to wheeze directly and also through postnatal life events, respiratory infections, and maternal depression.
- MeSH
- Child MeSH
- Cohort Studies MeSH
- Infant MeSH
- Humans MeSH
- Odds Ratio MeSH
- Respiratory Sounds * etiology MeSH
- Risk Factors MeSH
- Pregnancy MeSH
- Prenatal Exposure Delayed Effects * epidemiology MeSH
- Check Tag
- Child MeSH
- Infant MeSH
- Humans MeSH
- Pregnancy MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
When modeling auditory responses to environmental sounds, results are satisfactory if both training and testing are restricted to datasets of one type of sound. To predict 'cross-sound' responses (i.e., to predict the response to one type of sound e.g., rat Eating sound, after training with another type of sound e.g., rat Drinking sound), performance is typically poor. Here we implemented a novel approach to improve such cross-sound modeling (single unit datasets were collected at the auditory midbrain of anesthetized rats). The method had two key features: (a) population responses (e.g., average of 32 units) instead of responses of individual units were analyzed; and (b) the long sound segment was first divided into short segments (single sound-bouts), their similarity was then computed over a new metric involving the response (called Stimulus Response Model map or SRM map), and finally similar sound-bouts (regardless of sound type) and their associated responses (peri-stimulus time histograms, PSTHs) were modelled. Specifically, a committee machine model (artificial neural networks with 20 stratified spectral inputs) was trained with datasets from one sound type before predicting PSTH responses to another sound type. Model performance was markedly improved up to 92%. Results also suggested the involvement of different neural mechanisms in generating the early and late responses to amplitude transients in the broad-band environmental sounds. We concluded that it is possible to perform rather satisfactory cross-sound modeling on datasets grouped together based on their similarities in terms of the new metric of SRM map.
- MeSH
- Acoustic Stimulation methods MeSH
- Models, Biological * MeSH
- Inferior Colliculi physiology MeSH
- Rats MeSH
- Neural Networks, Computer * MeSH
- Neurons physiology MeSH
- Rats, Sprague-Dawley MeSH
- Evoked Potentials, Auditory, Brain Stem physiology MeSH
- Systems Biology MeSH
- Animals MeSH
- Check Tag
- Rats MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
BACKGROUND AND OBJECTIVES: Cardiovascular diseases are critical diseases and need to be diagnosed as early as possible. There is a lack of medical professionals in remote areas to diagnose these diseases. Artificial intelligence-based automatic diagnostic tools can help to diagnose cardiac diseases. This work presents an automatic classification method using machine learning to diagnose multiple cardiac diseases from phonocardiogram signals. METHODS: The proposed system involves a convolutional neural network (CNN) model because of its high accuracy and robustness to automatically diagnose the cardiac disorders from the heart sounds. To improve the accuracy in a noisy environment and make the method robust, the proposed method has used data augmentation techniques for training and multi-classification of multiple cardiac diseases. RESULTS: The model has been validated both heart sound data and augmented data using n-fold cross-validation. Results of all fold have been shown reported in this work. The model has achieved accuracy on the test set up to 98.60% to diagnose multiple cardiac diseases. CONCLUSIONS: The proposed model can be ported to any computing devices like computers, single board computing processors, android handheld devices etc. To make a stand-alone diagnostic tool that may be of help in remote primary health care centres. The proposed method is non-invasive, efficient, robust, and has low time complexity making it suitable for real-time applications.
- MeSH
- Humans MeSH
- Heart Diseases * diagnostic imaging MeSH
- Neural Networks, Computer MeSH
- Heart Sounds * MeSH
- Machine Learning MeSH
- Artificial Intelligence MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
Aim:Attracting and maintaining human resources is one of the most significant responsibilities in human resources management. The aim of this research was to study the effect of Quality of Work Life and Job Control on Turnover Intention and Organizational Indifference. Design: Cross-sectional descriptive study. Methods:The survey was conducted using four standard questionnaires. Data were collected from 395 nurses in Iranian public educational hospitals in 2017. SPSS and Amos 22.0 were used to analyze data andtest the theoretical model at a level of significance of 0.5.Results:The results indicated an average level of Quality of Work Life and Job Control, while the Turnover Intention level was higher than average, and participants reported a low level of Organizational Indifference. All hypotheses (except No. 2) were statistically significant, and the fitness indices [κ2=14.82 (df = 6;p = 0.037); κ2/df = 2.47; CFI = 0.94; IFI = 0.94; SRMR = 0.06; RMSEA = 0.06] indicated the soundness of the model. Conclusion:Planning properly and effectivelywith regard to Quality of Work Life and Job Control can play a significant role in the retention and performance of nurses –serious concerns for health policy makers. Nursing policy makers and managers can use these results to increase the number of nurses intending to remain in the profession.
Evoked potentials (EPs) reflect neural processing and are widely used to study sensory perception. However, methods of analyzing EP have been limited mostly to the conventional ensemble averaging of EP response trials to a repeated stimulus, and less so to single-trials analysis. Here we applied a new approach - functional data analysis (FDA) - to study auditory EP in the rat model of tinnitus, in which overdoses of salicylate (SS) are known to alter sound perception characteristically, as the same way as in humans. Single-trial auditory EPs were analyzed, after being collected on a daily basis from an awake rat, which had been surgically implanted with intracranial electrodes over its auditory cortex. Single-trial EP integrals were generated with sound stimuli (tones and clicks) presented systematically over an intensity range. The results were approximated using the cubic spline to give sets of smoothed response-level functions in dependence on the sound intensity. These functional data were analyzed using the methods of FDA. Comparisons between daily intensity series for each sound type were done using cross-distance measures based on the response-level functions in both the original form and the first-derivative form. From the results of FDA, the first-derivative form was found to provide a clearer separation when EP data from control groups were compared to the data from SS groups. This is also true when the daily data were compared within the more variable SS-group itself. In addition, at the high-intensity region where SS-action is presumably strong, we also observed characteristic changes in two statistical parameters, mean and skewness, of the cross-distance representations. Results suggested that FDA is a sensitive approach for EP studies, and it can become a powerful tool for the research in neural science, particularly neuropharmacology.
The aim of this study is the analysis of continuous speech signals of people with Parkinson's disease (PD) considering recordings in different languages (Spanish, German, and Czech). A method for the characterization of the speech signals, based on the automatic segmentation of utterances into voiced and unvoiced frames, is addressed here. The energy content of the unvoiced sounds is modeled using 12 Mel-frequency cepstral coefficients and 25 bands scaled according to the Bark scale. Four speech tasks comprising isolated words, rapid repetition of the syllables /pa/-/ta/-/ka/, sentences, and read texts are evaluated. The method proves to be more accurate than classical approaches in the automatic classification of speech of people with PD and healthy controls. The accuracies range from 85% to 99% depending on the language and the speech task. Cross-language experiments are also performed confirming the robustness and generalization capability of the method, with accuracies ranging from 60% to 99%. This work comprises a step forward for the development of computer aided tools for the automatic assessment of dysarthric speech signals in multiple languages.
- MeSH
- Speech Acoustics MeSH
- Reading MeSH
- Adult MeSH
- Phonetics MeSH
- Language * MeSH
- Middle Aged MeSH
- Humans MeSH
- Parkinson Disease diagnosis physiopathology MeSH
- Area Under Curve MeSH
- Speech physiology MeSH
- Recognition, Psychology MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Geographicals
- Czech Republic MeSH
- Germany MeSH
- Spain MeSH
