Diseño y validación de un instrumento para medir la autoeficacia para lactar de mujeres embarazadas mexicanas / Design and validation of a scale to measure breastfeeding self-efficacy in Mexican pregnant women

Resumen La lactancia materna exclusiva (LME) es una de las conductas saludables con mayor valor protector para la salud del niño y de la madre. La autoeficacia es un predictor de diferentes conductas saludables. El objetivo de esta investigación fue diseñar y validar un instrumento para medir la autoeficacia para lactar en mujeres embarazadas. Se realizó un estudio instrumental con usuarias de dos centros de salud de primer nivel de atención, seleccionadas a través de un muestreo no probabilístico. Se excluyeron aquellas mujeres que fueran analfabetas o tuvieran alguna condición médica que contraindicara la LME. Se utilizaron análisis descriptivos, bivariados y multivariados para obtener las propiedades psicométricas del instrumento. Participaron 369 mujeres. A través de un análisis factorial exploratorio se obtuvo una estructura unidimensional de 15 reactivos que explicó el 83% de la varianza total del instrumento (alfa de Cronbach = .93). Además, la autoeficacia para lactar se asoció (p <.05) con la intención para lactar, la edad y la escolaridad. El instrumento de autoeficacia para lactar obtenido mostró propiedades psicométricas adecuadas por lo que puede ser útil para identificar a las mujeres que están en riesgo de no iniciar la LME desde el nacimiento de su hijo, además, parece ser el primer instrumento de autoeficacia para lactar en México.

Abstract Exclusive breastfeeding (EB) is a health behavior with a greatest health protective value for children and mothers. Self-efficacy is a predictor of different health behaviors. The objective of this research was to design and validate an instrument to measure self-efficacy to breastfeed in pregnant women. An instrumental study was conducted with users of two primary health care centers, selected through a non-probabilistic sampling. Those women who were illiterate or had a medical condition that contraindicated EB were excluded. Descriptive, bivariate and multivariate analysis were used to obtain the psychometric properties of the instrument. 369 women participated. An exploratory factorial analysis resulted in a 15 items unidimensional structure that explained 83% of the total variance of the scale (Cronbach's alpha = .93). In addition, self-efficacy for breastfeeding was associated (p<.05) with intention to breastfeed, age and scholarship. The breastfeeding self-efficacy scale obtained showed adequate psychometric properties. So, it can be useful to identify women who may be at risk of not initiate breastfeeding from birth, as well as, it seems to be the first breastfeeding self-efficacy scale in Mexico.

Complexity-based analysis for the detection of heart murmurs.

While a healthy human heart produce a rhythmic pattern of sounds, some heart disorder induce deviations perceived as abnormal sounds called murmurs. Despite many murmurs can be considered harmless, other constitute the first basis of a heart disorder. In this sense, a correct diagnosis remains essential; however, due to the subjectivity on using human ear to make diagnosis, automatic detection systems appear as useful tools for helping medical specialists on improving diagnosis accuracy. Complexity analysis has become one important tool for the study of physiological signals, because tracking sudden alteration on the inherent complexity on biological processes might be useful for detecting pathologies. The present paper presents a complexity-based analysis methodology, which uses regularity features for the detection of heart murmurs, including Approximate Entropy, Sample Entropy, Gaussian Kernel Approximate Entropy, and Fuzzy Entropy. The results show the high discriminative power, up to 90%, of the Gaussian Kernel Approximate Entropy and Fuzzy Entropy for the proposed labour.

Evaluation of feature extraction techniques on event-related potentials for detection of attention-deficit/hyperactivity disorder.

Event-related potentials (ERPs) are one of the most informative and dynamic methods of monitoring cognitive processes, which are widely used in clinical research to deal a variety of psychiatric and neurological disorders as attention-deficit/hyperactivity disorder (ADHD). This work proposes an extraction and selection methodology for discriminating between normal and pathological patients with ADHD by using ERPs. Three different sets of features (morphological, wavelets, and nonlinear based) are analyzed, looking for the best classification accuracy. The results show that the wavelet features provided a good discriminative capability, but it improved by combining all the set of features and applying a feature selection algorithm, reaching a maximum accuracy rate of 91.3%.