Phenomenological variables of autobiographical memory narrativesassociated with healthy habits in children / Variables fenomenológicas de narrativas de memoria autobiográfica asociadas a hábitos saludables en niños

Different studies relateself-defining memories (SDM) to psy-chological well-being and health. This study aims toanalyse the relation be-tween the phenomenological variables (e.g., emotional intensity, vividness etc.) involved in self-defining memories associated with health (HSDMs) and healthy habits in 262 children aged between 9 and 13 years. Partici-pants’ eating habits and physical activity events are associated with the emotionality of their HSDMs. Most of the HSDMs were declared to be experienced with their family members, and greater importance was at-tributed to those memories related to mothers. Significant features of re-trieved HSDM can be detected from construction of autobiographical memories supporting the development of a robust healthy self in children. As such, families and schools should facilitate life experiences that lead to the formation of vivid and detailed HSDMs given that this is likely to promote health-related behaviours.(AU)

Diferentes estudios relacionan los recuerdos autodefinidos (SDM) con el bienestar psicológico y la salud. Este estudio tiene como objetivo analizar la relación entre las variables fenomenológicas (p. ej., intensidad emocional, viveza, etc.) implicadas en los recuerdos autodefinidos asociados a la salud (HSDM) y los hábitos saludables en 262 niños de entre 9 y 13 años. Los hábitos alimentarios y los eventos de actividad física de los participantes están asociados con la emotividad de sus HSDM. La mayoría de los HSDM declararon ser vividos con sus familiares, y se atribuyó mayor importancia a aquellos recuerdos relacionados con las madres. Se pueden detectar características significativas del HSDM recuperado a partir de la construcción de recuerdos autobiográficos que respaldan el desarrollo de un yo saludable y robusto en los niños. Como tal, las familias y las escuelas deben facilitar experiencias de vida que conduzcan a la formación de HSDM vívidos y detallados, dado que es probable que esto promueva comportamientos relacionados con la salud.(AU)

Common Laboratory Parameters Are Useful for Screening for Alcohol Use Disorder: Designing a Predictive Model Using Machine Learning.

The diagnosis of alcohol use disorder (AUD) remains a difficult challenge, and some patients may not be adequately diagnosed. This study aims to identify an optimum combination of laboratory markers to detect alcohol consumption, using data science. An analytical observational study was conducted with 337 subjects (253 men and 83 women, with a mean age of 44 years (10.61 Standard Deviation (SD)). The first group included 204 participants being treated in the Addictive Behaviors Unit (ABU) from Albacete (Spain). They met the diagnostic criteria for AUD specified in the Diagnostic and Statistical Manual of mental disorders fifth edition (DSM-5). The second group included 133 blood donors (people with no risk of AUD), recruited by cross-section. All participants were also divided in two groups according to the WHO classification for risk of alcohol consumption in Spain, that is, males drinking more than 28 standard drink units (SDUs) or women drinking more than 17 SDUs. Medical history and laboratory markers were selected from our hospital's database. A correlation between alterations in laboratory markers and the amount of alcohol consumed was established. We then created three predicted models (with logistic regression, classification tree, and Bayesian network) to detect risk of alcohol consumption by using laboratory markers as predictive features. For the execution of the selection of variables and the creation and validation of predictive models, two tools were used: the scikit-learn library for Python, and the Weka application. The logistic regression model provided a maximum AUD prediction accuracy of 85.07%. Secondly, the classification tree provided a lower accuracy of 79.4%, but easier interpretation. Finally, the Naive Bayes network had an accuracy of 87.46%. The combination of several common biochemical markers and the use of data science can enhance detection of AUD, helping to prevent future medical complications derived from AUD.