New Epidemic of Malnutrition in Young Slovenian Athletes.

Objective: The syndrome of relative energy deficiency in sports (RED-S) is the result of a prolonged period of low energy availability in athletes and leads to the deterioration of health and physical performance. Our study aimed to investigate the prevalence of RED-S-related health and performance problems in young Slovenian athletes, comparing middle (14-17 years) with late (18-21 years) adolescents. Methods: We analysd data of 118 young athletes (61 females, 57 males) who had nutritional assessments. Statistical analysis was carried out to determine the prevalence of RED-S-related problems. RED-S was diagnosed using the Relative Energy Deficiency Tool and the Sports Clinical Assessment Tool. Nutrition-related risk factors for RED-S were assessed with the use of a questionnaire and analysis of a three-day food diary. Results: The majority of athletes had at least one RED-S-related health disorder. The number of health-related disorders was significantly higher in females 3.0 (0.2) compared to males 1.6 (0.2). It was also significantly higher in middle 2.6 (0.2) compared to 1.9 (0.3) late adolescents. Potential nutritional risk factors for RED-S were low carbohydrate intake, skipping meals before and after practice, a desire to lose weight, and a history of weight loss in the past year. Conclusion: The prevalence of health-related RED-S disorders and performance problems in young athletes is concerning, and our study indicates that middle adolescents are more vulnerable to this than late adolescents. Our findings suggest that screening for RED-S symptoms and nutrition-related risk factors for RED-S should be included in regular medical examination of young athletes.

Optimizing non-pharmaceutical intervention strategies against COVID-19 using artificial intelligence.

One key task in the early fight against the COVID-19 pandemic was to plan non-pharmaceutical interventions to reduce the spread of the infection while limiting the burden on the society and economy. With more data on the pandemic being generated, it became possible to model both the infection trends and intervention costs, transforming the creation of an intervention plan into a computational optimization problem. This paper proposes a framework developed to help policy-makers plan the best combination of non-pharmaceutical interventions and to change them over time. We developed a hybrid machine-learning epidemiological model to forecast the infection trends, aggregated the socio-economic costs from the literature and expert knowledge, and used a multi-objective optimization algorithm to find and evaluate various intervention plans. The framework is modular and easily adjustable to a real-world situation, it is trained and tested on data collected from almost all countries in the world, and its proposed intervention plans generally outperform those used in real life in terms of both the number of infections and intervention costs.

Using survey data to estimate the impact of the omicron variant on vaccine efficacy against COVID-19 infection.

Symptoms-based detection of SARS-CoV-2 infection is not a substitute for precise diagnostic tests but can provide insight into the likely level of infection in a given population. This study uses symptoms data collected in the Global COVID-19 Trends and Impact Surveys (UMD Global CTIS), and data on variants sequencing from GISAID. This work, conducted in January of 2022 during the emergence of the Omicron variant (subvariant BA.1), aims to improve the quality of infection detection from the available symptoms and to use the resulting estimates of infection levels to assess the changes in vaccine efficacy during a change of dominant variant; from the Delta dominant to the Omicron dominant period. Our approach produced a new symptoms-based classifier, Random Forest, that was compared to a ground-truth subset of cases with known diagnostic test status. This classifier was compared with other competing classifiers and shown to exhibit an increased performance with respect to the ground-truth data. Using the Random Forest classifier, and knowing the vaccination status of the subjects, we then proceeded to analyse the evolution of vaccine efficacy towards infection during different periods, geographies and dominant variants. In South Africa, where the first significant wave of Omicron occurred, a significant reduction of vaccine efficacy is observed from August-September 2021 to December 2021. For instance, the efficacy drops from 0.81 to 0.30 for those vaccinated with 2 doses (of Pfizer/BioNTech), and from 0.51 to 0.09 for those vaccinated with one dose (of Pfizer/BioNTech or Johnson & Johnson). We also extended the study to other countries in which Omicron has been detected, comparing the situation in October 2021 (before Omicron) with that of December 2021. While the reduction measured is smaller than in South Africa, we still found, for instance, an average drop in vaccine efficacy from 0.53 to 0.45 among those vaccinated with two doses. Moreover, we found a significant negative (Pearson) correlation of around - 0.6 between the measured prevalence of Omicron in several countries and the vaccine efficacy in those same countries. This prediction, in January of 2022, of the decreased vaccine efficacy towards Omicron is in line with the subsequent increase of Omicron infections in the first half of 2022.

Food Frequency Questionnaire Personalisation Using Multi-Target Regression.

Fondazione Bruno Kessler is developing a mobile app prototype for empowering citizens to improve their health conditions through different lifestyle interventions that will be incorporated into a mobile application for lifestyle promotion of the Province of Trento in the context of the Trentino Salute 4.0 Competence Center. The envisioned interventions are based on promoting behaviour change in various domains such as physical activity, mental health and nutrition. In particular, the nutrition component is a self-monitoring module that collects dietary habits to analyse them and recommend healthier eating behaviours. Dietary assessment is completed using a Food Frequency Questionnaire on the Mediterranean diet that is presented to the user as a grid of images. The questionnaire returns feedback on 11 aspects of nutrition. Although the questionnaire used in the application only consists of 24 questions, it still could be a bit overwhelming and a bit crowded when shown on the screen. In this paper, we tried to find a machine-learning-based solution to reduce the number of questions in the questionnaire. We proposed a method that uses the user's previous answers as additional information to find the goals that need more attention. We compared this method with a case where the subset of questions is randomly selected and with a case where the subset is chosen using feature selection. We also explored how large the subset should be to obtain good predictions. All the experiments are conducted as a multi-target regression problem, which means several goals are predicted simultaneously. The proposed method adjusts well to the user in question and has the slightest error when predicting the goals.

What Actually Works for Activity Recognition in Scenarios with Significant Domain Shift: Lessons Learned from the 2019 and 2020 Sussex-Huawei Challenges.

From 2018 to 2021, the Sussex-Huawei Locomotion-Transportation Recognition Challenge presented different scenarios in which participants were tasked with recognizing eight different modes of locomotion and transportation using sensor data from smartphones. In 2019, the main challenge was using sensor data from one location to recognize activities with sensors in another location, while in the following year, the main challenge was using the sensor data of one person to recognize the activities of other persons. We use these two challenge scenarios as a framework in which to analyze the effectiveness of different components of a machine-learning pipeline for activity recognition. We show that: (i) selecting an appropriate (location-specific) portion of the available data for training can improve the F1 score by up to 10 percentage points (p. p.) compared to a more naive approach, (ii) separate models for human locomotion and for transportation in vehicles can yield an increase of roughly 1 p. p., (iii) using semi-supervised learning can, again, yield an increase of roughly 1 p. p., and (iv) temporal smoothing of predictions with Hidden Markov models, when applicable, can bring an improvement of almost 10 p. p. Our experiments also indicate that the usefulness of advanced feature selection techniques and clustering to create person-specific models is inconclusive and should be explored separately in each use-case.

Is RED-S in athletes just another face of malnutrition?

BACKGROUND & AIMS: Relative energy deficiency syndrome in sport (RED-S) can impair the function of several body systems, resulting in short and long-term threats to athletes' health and performance. Research showed that these health and performance problems are often unrecognized, and the treatment is not adequate. The retrospective study presented in this paper aims to determine the prevalence of RED-S-related symptoms in a sample of Slovenian competitive athletes from various sports. METHODS: We performed retrospective research based on a database of 150 athletes, aged from 14 to 34, who had nutritional assessments as a part of their medical examination. Data were collected, refined and statistical analysis was performed. 77 women and 73 men were included; 113 were classified as young athletes (14-21 years) and 37 as elite athletes (more than 21 years). RESULTS: The majority (87%) of the athletes demonstrated at least one health-related symptom described by the RED-S-model; only 9% female and 18% male did not have any symptoms of RED-S. The number of different body systems with the compromised function was significantly higher (p < 0.001) in female athletes (2.9 ± 0.2) in comparison to male athletes (1.6 ± 0.1). For other health-related symptoms, there are statistically significant differences between young and elite athletes (p = 0.03), between female and male athletes (p = 0.02) and between young and elite female athletes (p = 0.01). When comparing groups by the number of all RED-S related symptoms, female athletes were more affected (p = 0.02). According to the RED-S CAT tool, the majority of athletes (64%) were classified in the yellow group, 7% of athletes have severe health and performance problems and fulfil criteria for the red group, and only 29% were classified in the green group. CONCLUSIONS: A high prevalence of RED-S-related symptoms in our sample competitive athletes indicates the high prevalence of nutrition-related medical problems in young and elite athletes. Therefore, it is necessary to incorporate nutritional risk screenings as a part of regular medical examinations of athletes. In addition, appropriate treatments for competitive athletes should be readily accessible, even for young athletes. It seems that the youth athlete population is the most endangered for developing malnutrition-related health problems. At the same time, we urgently need a more specific and simple nutritional screening tool that will allow us to identify athletes at nutritional risk or athletes who have RED-S.

Machine Learning for Analyzing Non-Countermeasure Factors Affecting Early Spread of COVID-19.

The COVID-19 pandemic affected the whole world, but not all countries were impacted equally. This opens the question of what factors can explain the initial faster spread in some countries compared to others. Many such factors are overshadowed by the effect of the countermeasures, so we studied the early phases of the infection when countermeasures had not yet taken place. We collected the most diverse dataset of potentially relevant factors and infection metrics to date for this task. Using it, we show the importance of different factors and factor categories as determined by both statistical methods and machine learning (ML) feature selection (FS) approaches. Factors related to culture (e.g., individualism, openness), development, and travel proved the most important. A more thorough factor analysis was then made using a novel rule discovery algorithm. We also show how interconnected these factors are and caution against relying on ML analysis in isolation. Importantly, we explore potential pitfalls found in the methodology of similar work and demonstrate their impact on COVID-19 data analysis. Our best models using the decision tree classifier can predict the infection class with roughly 80% accuracy.

Optimising an FFQ Using a Machine Learning Pipeline to teach an Efficient Nutrient Intake Predictive Model.

Food frequency questionnaires (FFQs) are the most commonly selected tools in nutrition monitoring, as they are inexpensive, easily implemented and provide useful information regarding dietary intake. They are usually carefully drafted by experts from nutritional and/or medical fields and can be validated by using other dietary monitoring techniques. FFQs can get very extensive, which could indicate that some of the questions are less significant than others and could be omitted without losing too much information. In this paper, machine learning is used to explore how reducing the number of questions affects the predicted nutrient values and diet quality score. The paper addresses the problem of removing redundant questions and finding the best subset of questions in the Extended Short Form Food Frequency Questionnaire (ESFFFQ), developed as part of the H2020 project WellCo. Eight common machine-learning algorithms were compared on different subsets of questions by using the PROMETHEE method, which compares methods and subsets via multiple performance measures. According to the results, for some of the targets, specifically sugar intake, fiber intake and protein intake, a smaller subset of questions are sufficient to predict diet quality scores. Additionally, for smaller subsets of questions, machine-learning algorithms generally perform better than statistical methods for predicting intake and diet quality scores. The proposed method could therefore be useful for finding the most informative subsets of questions in other FFQs as well. This could help experts develop FFQs that provide the necessary information and are not overbearing for those answering.