297 research outputs found

    Changes in Sport Nutrition Knowledge, Attitudes/Beliefs and Behaviors Following a Two-Year Sport Nutrition Education and Life-Skills Intervention among High School Soccer Players

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    The purpose of this study was to examine the impact of a sport nutrition education and life-skills intervention on sport nutrition knowledge (SNK), attitudes/beliefs and dietary behaviors relevant to sport nutrition among high school (HS) soccer players. Three assessments were done over the 2-year intervention (baseline = time 1, end year 1 = time 2, end year 2 = time 3). Participants (n = 217; females = 64%; Latino = 47.5%; 14.9 ± 0.9-year; 46.5% National School Breakfast/Lunch Program) were assigned to an intervention group (IG, n = 153; 9 schools) or comparison group (CG, n = 64; 4 schools) based on geographical location. Differences over time were examined based on group, sex, socioeconomic status (SES) and race/ethnicity. The IG increased SNK scores by ~10% (time 1 = 51.6%; time 3 = 60.9%; p ≀ 0.001), with the greatest change in the female IG vs. CG and no differences in male IG vs. CG. Daily breakfast consumption was 53.7% in both groups. IG players were 3 times more likely (95%CI = 2.59, 7.77) to report trying to eat for performance (IG = 48.7% vs. CG = 30.2%). By time 3, IG players were less likely to report that \u27diet met nutritional requirements\u27 (31.6%) compared to CG (47.6%). For IG, the consumption of lunch (≄5-days/week) did not change (92.2⁻93.4%), but declined in the CG (90.6%) (p = 0.04). No other differences by sub-population (race/ethnicity, SES) were observed. Our findings indicate that HS athletes are motivated to learn and improve diet behaviors, and benefit from team-based nutrition interventions. Future interventions should consider delivery of curriculum/experiential learning during a defined training period, with messages reinforced with supports at home, school and athletic settings

    WAVE Project: Sport Nutrition Education Resources

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    The WAVE~Ripples for Change: Obesity Prevention in Active Youth (WAVE) project’s primary objective is to prevent unhealthy weight gain among high school athletes through healthy eating and reduced sedentary time. Educators are familiar with the myriad of challenges in presenting nutrition, diet, and physical activity information to high school students. WAVE uses adolescent athletes’ interest in sport to draw them into the topic of sport nutrition and healthy eating; helping them apply the knowledge and skills they learned in class, on the field, and in their lives. WAVE developed and field-tested an after-school program for high school athletes that includes 7 sport nutrition lessons (30 to 45 minutes each) and 3 team-building, family and consumer sciences life-skill workshops. WAVE also developed a cloud-based data management system to support the tracking of learner profiles, survey administration, big data visualization, and automated health report generation

    Coinfections by noninteracting pathogens are not independent and require new tests of interaction.

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    If pathogen species, strains, or clones do not interact, intuition suggests the proportion of coinfected hosts should be the product of the individual prevalences. Independence consequently underpins the wide range of methods for detecting pathogen interactions from cross-sectional survey data. However, the very simplest of epidemiological models challenge the underlying assumption of statistical independence. Even if pathogens do not interact, death of coinfected hosts causes net prevalences of individual pathogens to decrease simultaneously. The induced positive correlation between prevalences means the proportion of coinfected hosts is expected to be higher than multiplication would suggest. By modelling the dynamics of multiple noninteracting pathogens causing chronic infections, we develop a pair of novel tests of interaction that properly account for nonindependence between pathogens causing lifelong infection. Our tests allow us to reinterpret data from previous studies including pathogens of humans, plants, and animals. Our work demonstrates how methods to identify interactions between pathogens can be updated using simple epidemic models
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