Configuración teórica de la motivación de salud desde la teoría de la autodeterminación

El estudio de la motivación humana es un constructo altamente complejo y con una gran variabilidad de enfoques. La teoría de la autodeterminación (TAD) ha demostrado una relativa efectividad y consistencia en muchos aspectos relacionados con la salud, como por ejemplo el ejercicio físico, la alimentación, el sueño, el bienestar psicológico o el consumo de tabaco. Las investigaciones muestran que la motivación autodeterminada se corresponde con la motivación intrínseca y en cambio la motivación extrínseca y sus formas de regulación pueden corresponderse con comportamiento no autodeterminados, pudiendo llegar hasta la desmotivación. En este trabajo se formula una construcción teórica sobre este modelo, introduciendo la percepción de riesgo (PR) y la vulnerabilidad percibida (VP) como elementos que pueden variar el sentido final de la motivación e incluso mejorar alguna de sus regulaciones extrínsecas y la desmotivación. Una de las posibilidades teóricas que sugerimos para intentar neutralizar los tipos no autodeterminados es procurar aumentar la PR y la VP de la persona, ya que estando estas dos variables altas, la probabilidad de que la desmotivación aparezca se reduce significativamente, y las acciones forzadas de la regulación externa y la regulación introyectada pueden amortiguarse y aumentar la internalización lo que podría favorecer los comportamientos de salud.

Effects of a training program to enhance autonomy supportive behaviors among youth soccer coaches

This study aimed to evaluate the prevalence and implementation of a training emphasizing the use of autonomy supportive coaching behaviors among youth soccer coaches in game-play situations as well as evaluating its effects on motivational processes among athletes. Participants included youth sport soccer coaches and their intact teams. Coaches received a series of autonomy-supportive coaching training interventions based on successful programs in general and physical education (Reeve, Jang, Carrell, Jeon & Barch, 2004; Cheon, Reeve & Moon, 2012). Athletes completed questionnaires to assess perceived autonomy support, basic need satisfaction, and motivation (Harris & Watson, 2011). Observations indicated coaches were not able to significantly modify their behaviors, yet reflectively reported modest implementation of autonomy supportive behaviors. Coaches believed the training influenced their coaching style/philosophy in regards to the coach-athlete relationship and communication styles, emphasizing choice and rationales. Continued research is needed to enhance use of autonomy supportive behaviors with volunteer coaches in a youth sport environment.

Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps

Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.