M.M. Heinen

Low Energy Availability in Athletes: A Review of Prevalence, Dietary Patterns, Physiological Health, and Sports Performance

In a high-performance sports environment, athletes can present with low energy availability (LEA) for a variety of reasons, ranging from not consuming enough food for their specific energy requirements to disordered eating behaviors. Both male and female high-performance athletes are at risk of LEA. Longstanding LEA can cause unfavorable physiological and psychological outcomes which have the potential to impair an athlete’s health and sports performance. This narrative review summarizes the prevalence of LEA and its associations with athlete health and sports performance. It is evident in the published scientific literature that the methods used to determine LEA and its associated health outcomes vary. This contributes to poor recognition of the condition and its sequelae. This review also identifies interventions designed to improve health outcomes in athletes with LEA and indicates areas which warrant further investigation. While return-to-play guidelines have been developed for healthcare professionals to manage LEA in athletes, behavioral interventions to prevent the condition and manage its associated negative health and performance outcomes are required.

Screening for risk of low energy availability in athletic and recreationally active females in Ireland

ABSTRACT Introduction: Low energy availability (LEA) results in physiological adaptations, which can contribute to unfavourable health outcomes. Little information exists on risk of LEA in active individuals competing in different sports or levels of competition. The aims of this study were to (1) identify risk of LEA in females competing at different levels of competition and (2) investigate associations between risk of LEA, illness and dietary habits. Methods: The validated questionnaire, ‘Low Energy Availability in Females Questionnaire’ was distributed online (November 2016–February 2017) to assess risk of LEA. Twenty-nine additional questions collected information on demographics, illness history and dietary habits. Participants were considered at risk of LEA if they attained a score of ≥ 8 and were grouped into: (i) international; (ii) provincial/inter-county; (iii) competitive; and (iv) recreationally active. Chi-square and logistic regression analyses were used to explore differences between those at risk or not at risk of LEA. Results: Risk of LEA was identified in 40% (n = 331) of 833 participants and was 1.7 and 1.8 times more likely in international and provincial/inter-county athletes compared to those who were recreationally active (International: odds ratios (OR) 1.68, 95% confidence intervals (95%CI) 1.12–2.54; Provincial/inter-county: OR 1.83, 95%CI 1.20–2.77). In participants at risk of LEA, missing >22 days of training during the previous year due to illness occurred 3 times more frequently (OR 3.01, 95%CI 1.81–5.02). Conclusion: Risk of LEA was widespread in this heterogeneous sample. Awareness of LEA and the development of appropriate energy management strategies to ensure athlete health across levels of competition are required.

P43 The Healthy Worker Effect? Socio-economic and health status determinants of changing self-rated health in older people in the Lifeways Cross-Generation Study of a Thousand Families

Background There has been renewed interest in the self-rated health (SRH) of older people. Many cohorts, including the Lifeways Cross Generation Cohort Study, have confirmed the role of SRH in predicting mortality. Many have also examined cross-sectional determinants, but few have described the determinants of change in SRH using the same measures 10 years apart, with a particular interest in assessing the relative contributions of change in physical and psychological morbidity and socio-demographic changes. Methods Using generalised estimating equations (GEE), we examine predictors of SRH in Lifeways grandparents who participated in the baseline (n = 710) and year 10 follow-up (n = 843) questionnaire surveys. Morbidity score was determined from summing 6 physician-diagnosed conditions that were self-reported at both time-points. For the cross sectional measures, a binomial distribution with logit link was specified, examining predictors of excellent/very good/good SRH versus fair or poor. For the predictors of change in SRH (range −2 to +2) a linear distribution with identity link was specified and two multivariable GEE models were constructed, firstly with only baseline variables, and secondly with variables reflecting change in morbidity or socioeconomic variables measured at both time-points. Each term in the model was examined conditional on the baseline value of SRH. Results Cross sectional results at both baseline and year 10 confirm that measures of both physical and psychological morbidity, as well as a measure of socio-economic deprivation (eligibility for means-tested General Medical Services – GMS), are predictive of SRH in this cohort. Of the 288 respondents with SRH measured at both time-points the following were significant predictors in the baseline GEE model (B, p): age (−0.018, 0.017), Eastern (urban) region (−0.223, 0.011) and baseline SRH (0.411, <0.001). In the final GEE model, the following were significant (B, p): age (−0.018, 0.011), Eastern region (−0.196, 0.023), baseline SRH (0.337, <0.001), change in morbidity score (0.203, <0.001), developing an activity limiting health condition (−0.701, <0.001), becoming GMS eligible (−0.239, 0.012) and change in employment status from working to other (0.234, 0.011). Conclusion The results of this on-going analysis show some interesting and novel findings. Change in SRH is related to initial level of SRH, but not initial level of morbidity. Even after accounting for change in objective and subjective morbidity, certain socio-economic factors (region and GMS eligibility) are influencing change in SRH. Of particular interest and meriting further investigation is the finding that leaving employment is associated with a positive change in SRH.

P58 Trajectories of Body Mass Index By Age 9 in The Lifeways Cross Generation Cohort Study Children in The Republic Of Ireland

Background The global childhood obesity epidemic poses great challenges in understanding how growth trajectories are determined from early life and across generations. It is increasingly clear that factors causing intra-uterine growth retardation and macrosomia may place children on adverse growth trajectories that lead to overweight and obesity. The Lifeways study was established to study the influence of familial and lifestyle factors including diet on long-term outcomes for children. Methods Of 1082 mother-child pairs recruited antenatally in 2001–3, after exclusion of twins, data were available for 981 children with at least one body mass index (BMI) measurement available at one of 3 waves of follow-up to age of 9 years old. Infants were categorised into low (<2.5 kg, N = 48), normal (2.5–4 kg, N = 761) and high (≥4 kg, N = 172) birth weight categories. We examined a variety of maternal and child characteristics at univariate level in relation to the trajectory groups. A linear mixed model was fitted with wave and birth weight category as factors with a fixed intercept and allowing correlated residuals over time with an unstructured covariance matrix (selected by BIC) and with an interaction term to allow for different trajectories over time, both adjusted and unadjusted for gestational age. Results There was a positive association for maternal age (p < 0.001), pre-pregnancy BMI (p < 0.001), and an inverse association for maternal smoking status (p < 0.001) with these trajectory groups. Maternal fat intake (% Energy Intake) was higher and carbohydrate intake (% Energy Intake) was lower during pregnancy in the low birth-weight group, p = 0.004 and p = 0.008 respectively. There was a statistically significant interaction (p < 0.001), indicating a different pattern of change in trajectory over time in the three groups, whether adjusted for gestational age or not. The low birth weight group showed a rapid catch-up response by age 5, before overtaking the normal weight group by age 9. The high birth weight group continued to track higher than the normal weight group over time. Conclusion These novel prospective cohort data confirm the adverse influence of birth-weight outcomes on subsequent growth trajectories in this cohort of children, in turn associated with maternal characteristics during gestation and pregnancy.

Prevalence of overweight children aged 7 years: Results of the World Health Organisation Childhood Growth Surveillance Initiative in the Republic of Ireland

. The World Health Organisation (WHO) Childhood Obesity Surveillance Initiative wasestablished in 2008 to measure systematically childhood obesity in the European region. The present study provides an assessment of theprevalence of obesity from three sweeps of 7 year old Irish children measured in 2008, 2010 and 2012. The core objective was to measureweight, height and waist circumference in primary school children according to the WHO protocol