Weight Loss Strategies in Male Competitors of Combat Sport Disciplines

Background and objective: The use of suboptimal weight loss strategies in order to reach specific weight ranges as observed in combat sport disciplines can give rise to severe health problems. However, particular aspects regarding management of weight category comparing three sport disciplines remain to be investigated. Therefore, the aim of the present study was to obtain information regarding the weight loss strategies that competitors performed before a tournament. Materials and Methods: This article describes the most common dietary-nutritional strategies used by 140 national university male competitors of judo (n = 52), karate (n = 40) and taekwondo (n = 48) in order to achieve a specific weight, according to the rapid weight loss questionnaire (RWLQ) and the EAT-27 questionnaire. Results: Around 50% of participants were not involved in a weight loss process. Among the remaining participants, we considered three periods for weight reduction: less than 1 week (35% in judo, 8% in karate and 19% in taekwondo), less than 1 month (17% in judo, 15% in karate and 26% in taekwondo) and more than 1 month (0% in judo, 5% in karate and 21% in taekwondo). Severe fasting, focused on food/water restriction, was the most commonly used strategy, being more frequent in judo players. Light weight judo practitioners generally lost 2–5 kg before the contest. One third of participants avoided carbohydrate consumption when performing food restriction. Finally, individuals that reduced weight in the last week seemed to develop an unhealthy psychological relationship with food. Conclusion: All these aspects could be particularly relevant, providing information regarding how competitors manage basic nutritional concepts that guide dieting strategies. This information is relevant to prepare future educational interventions in the area of nutrition for competitors, coaches and technical staff.

Social Jetlag and Emerging Adults’ Performance in a Behavioral Weight Loss Trial

Social jetlag (SJ)—the shift in sleep timing between workdays and free days—is linked to deleterious cardiometabolic outcomes. SJ is greatest among emerging adults, who are already at high risk for overweight/obesity and experience suboptimal weight loss outcomes. Goals of this ancillary study were to assess SJ among emerging adults enrolled in a 6-month behavioral weight loss trial and examine the association between SJ and treatment outcomes. Bedtime/waketime were self-reported at baseline, and program engagement was monitored throughout the intervention. Weight, waist circumference, and body fat percentage were measured at baseline and post-treatment. Participants (N = 282) reported 1.5 hours of SJ on average, with 30.5% reaching the threshold for clinical significance. There were no significant associations between SJ and program engagement nor between SJ and change in adiposity. Life transitions and chaotic schedules are common during emerging adulthood; thus, further research is needed to capture nuanced patterns of sleep disruption.

Connectome-Based Prediction of Optimal Weight Loss Six Months After Bariatric Surgery

Despite bariatric surgery being the most effective treatment for obesity, a proportion of subjects have suboptimal weight loss post-surgery. Therefore, it is necessary to understand the mechanisms behind the variance in weight loss and identify specific baseline biomarkers to predict optimal weight loss. Here, we employed functional magnetic resonance imaging (fMRI) with baseline whole-brain resting-state functional connectivity (RSFC) and a multivariate prediction framework integrating feature selection, feature transformation, and classification to prospectively identify obese patients that exhibited optimal weight loss at 6 months post-surgery. Siamese network, which is a multivariate machine learning method suitable for small sample analysis, and K-nearest neighbor (KNN) were cascaded as the classifier (Siamese-KNN). In the leave-one-out cross-validation, the Siamese-KNN achieved an accuracy of 83.78%, which was substantially higher than results from traditional classifiers. RSFC patterns contributing to the prediction consisted of brain networks related to salience, reward, self-referential, and cognitive processing. Further RSFC feature analysis indicated that the connection strength between frontal and parietal cortices was stronger in the optimal versus the suboptimal weight loss group. These findings show that specific RSFC patterns could be used as neuroimaging biomarkers to predict individual weight loss post-surgery and assist in personalized diagnosis for treatment of obesity.