Background: Artificial Neural Networks (ANNs), particularly multilayer perceptrons (MLPs) with backpropagation, are increasingly used in Behavioral and Health Sciences for data analysis. This paper provides a comprehensive tutorial on implementing backpropagation in MLP models for regression and classification tasks using Python. Method: The tutorial guides readers step-by-step through building a backpropagation MLP using a simulated data matrix (N = 1,000) with psychological variables, demonstrating ANNs’ versatility in predicting continuous variables and classifying (binary and polytomous) patterns. Python scripts and detailed output interpretations are included. Results: MLP models trained with backpropagation show effectiveness in regression (R² = .71) and classification (binary AUC = .93, polytomous AUC range: .81-.93) on test sets. Conclusions: This tutorial aims to demystify ANNs and promote their use in Behavioral and Health Sciences and other fields, bridging the gap between theory and practical implementation. Introducción:, Las Redes Neuronales Artificiales (RNA), especialmente los perceptrones multicapa (MLPs) con retropropagación, son cada vez más utilizadas en Ciencias del Comportamiento y de la Salud para analizar datos. Este artículo presenta un tutorial completo sobre la implementación de modelos MLP con retropropagación para tareas de regresión y clasificación usando Python. ,Método:, El tutorial guía paso a paso la construcción de un MLP con retropropagación utilizando una matriz de datos simulados (,N, = 1,000) con variables psicológicas, demostrando la versatilidad de las RNA en la predicción de variables continuas y clasificación de (binarios y politómicos). Se incluyen scripts de Python y su interpretación detallada. ,Resultados:, Los modelos MLP muestran eficacia en regresión (,R,² = 0.71) y clasificación (AUC binaria = .93, rango AUC politómica: .81-.93) en los tests. ,Conclusiones:, Este tutorial persigue desmitificar las RNA y promover su uso en Ciencias del Comportamiento y la Salud, facilitando la transición de la teoría a la práctica.

The pressure that coaches usually apply on their athletes, alongside injuries and the lack of enjoyment during sport practice, are key factors involved in sports abandonment. Behavioral interventions through psychoeducational programs have proven to be useful to improve coaching behaviors, providing appropriate context in sports. This study aims to assess a brief psychological intervention in football coaches to reduce potentially harmful verbal responses and increase the use of positive feedback. Our research was conducted in an amateur football club where 15 coaching staff teams agreed to participate. The experimental design of this study is a randomized controlled trial with control (n=7) and experimental (n=8) groups with delayed intervention. Verbal responses from coaches were collected as count data with an adapted version of the Coaching Behavior Assessment System. Data was analyzed using the Poisson Regression Model. The results show a reduction in hostile feedback by 87.7% (CI = 71% - 94.9%), technical instructions with ball in play by 39.8% (CI = 24.2% - 52.2%), complaints to referees by 95.8% (CI = 82.8% - 99.3%) and an increase in praise contingent with successful action y 87% (CI = 13% - 209%) after the intervention. These findings have important implications for the implementation of programs that seek to improve delivery of instructions in sport coaches, providing a thorough procedure for the shaping of the verbal response. Valuable insights to foster a supportive learning environment for adolescents and young athletes are addressed in the discussion. Keywords: Psychological intervention, Behavioral intervention, Sport coach, Verbal response, Technical instructions, Randomized controlled trial, Experimental design, Coaching Behavior Assessment System.

Physical activity (PA) has been associated with cognitive performance in children, although the underlying mechanisms remain unclear. Objective: This study examined whether perceived physical fitness mediates the association between PA and cognitive outcomes, while exploring sex differences. Sex‐stratified multigroup structural equation modeling was conducted using cross‐sectional baseline data from the Break4Brain project ( n = 722 [354 girls], aged 10–12). PA was not associated with cognitive flexibility in either sex. In girls, PA was negatively associated with selective attention ( β = −0.22, p = 0.002), whereas no significant associations with cognitive outcomes were observed in boys. PA was positively associated with perceived physical fitness in boys ( β = 0.51, p < 0.001) and girls ( β = 0.56, p < 0.001). In girls, perceived physical fitness was positively associated with selective attention ( β = 0.35, p < 0.001) and significantly mediated the relationship between PA and selective attention ( β = 0.17, 95% CI [0.08, 0.26]). These findings suggest sex‐specific pathways linking PA, perceived physical fitness, and cognitive performance.

Background/Objectives: Mobile health (mHealth) interventions can enhance chronic disease management, but their integration into public healthcare systems remains complex. DiabeText is the first SMS-based intervention in Spain delivering personalized diabetes self-management support using electronic health record data. This study explored perceived barriers and facilitators to the implementation of DiabeText in the Spanish public health context from the perspective of key stakeholders. Methods: A qualitative study was conducted using semi-structured interviews with 14 purposively selected stakeholders involved in digital health, diabetes care, data protection, and healthcare management across several Spanish regions. Interviews were thematically analyzed using Braun and Clarke’s approach and guided by the Implementation Research Logic Model. Results: Participants reported several barriers, including concerns regarding data protection, uncertainty about long-term sustainability, insufficient training and engagement of healthcare professionals and low digital literacy among certain patient groups. Facilitators included favorable institutional momentum for digital innovation, funding availability, perceived clinical utility and scalability of DiabeText, and growing patient familiarity with digital tools. Recommended strategies included integration into existing healthcare systems and workflows, professional training and use of familiar communication platforms. Conclusions: Effective implementation of DiabeText requires addressing regulatory, organizational, and equity-related barriers while leveraging institutional support and readiness for innovation. Early involvement of healthcare professionals, robust data governance, and investment in digital literacy are essential to ensure sustainable and equitable adoption. These findings provide actionable insights to support the integration of mHealth tools into chronic disease care in Spain and similar settings.