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Physical activity (PA) promotion is a complex challenge, with the Global Action Plan on Physical Activity (GAPPA) endorsing a systems approach and recommending countries assess existing areas of progress which can be strengthened. This paper reports a process facilitating a systems approach for identifying current good practice and gaps for promoting PA in Ireland. Elements of participatory action research were enabled through 3 stages: (1) aligning examples of actions from Irish policy documents (n = 3) to the GAPPA, (2) workshop with stakeholders across multiple sectors, and (3) review of outputs. Data collected through the workshop were analyzed using a deductive thematic analysis guided by the GAPPA. The policy context in Ireland aligns closely to the GAPPA with the creation of Active Systems the most common strategic objective across policy documents. Forty participants (50% male) took part in the systems approach workshop, which after revision resulted in 80 examples of good practice and 121 actions for greater impact. A pragmatic and replicable process facilitating a systems approach was adopted and showed current Irish policy and practices align with the GAPPA "good practices." The process provides existing areas of progress which can be strengthened, as well as the policy opportunities and practice gaps.

Background: Students engage in risky health-related behaviours that influence their current and future health status. Health-related behaviours cluster among adults and differently based on sub-populations characteristics but research is lacking for university populations. Examining the clustering of health- related behaviours can inform our initiatives and strategies, while examining cluster members' characteristics can help target those who can prosper most from health promotion efforts. This study examines the clustering of health-related behaviours in Irish university students, and investigates the relationship with students' sex, age, field of study and accommodation type., Methods: An online survey was completed by 5672 Irish university students (51.3% male; 21.60 ± 5.65 years) during 2014. Two-step cluster analysis was used to understand how health-related behaviours (physical activity, smoking, alcohol intake, drug use and dietary habits) cluster among male and female students. Binary logistic regressions were conducted to examine the likelihood of students falling into certain clusters based on their characteristics., Results: Five cluster groups were identified in males and four in females. A quarter of males were categorised as ideal healthy with older students and those from certain fields of study having a higher likelihood of being classified in a low physical activity and poor diet (OR = 1.06-2.89), alcohol consumption (OR = 1.03-3.04), or smoking and drug use (OR = 1.06-2.73) cluster. Forty-five percent of females were categorised as ideal healthy with older females more likely to be in a low active and smoking cluster (OR = 1.03), and less likely to be in a convenience food cluster (OR = 0.96). Females from certain fields of study were also more likely to be classified in these clusters (OR = 1.59-1.76). Students living away from their family home had in increased likelihood of being in a cluster related to a higher frequency of alcohol consumption (OR = 1.72-3.05)., Conclusion: Health-related behaviours cluster among this population and need to be taken into account when designing multi-health interventions and policies. These findings can be used to target student groups at risk, leading to more efficient and successful health promotion efforts. The addition of modules providing information regarding health-related behaviours are advised in all fields of study.

Background: Knowledge of the physical activity correlate profile of adolescent females will provide insight into decreasing physical activity patterns among adolescent females., Methods: Correlates of physical activity and physical activity stage of change were assessed during 2007-2008 among 871 Irish adolescent females in years 1-6 in secondary schools (15.28 ± 1.8 years). Multivariate Analysis of Variance was used to identify whether differences in correlates of physical activity could be detected across year in school and physical activity stages of change., Results: Significant differences (P < .01) were found in 11 of the 16 measured correlates across year in school and in 14 of the 16 correlates across stage of change. Effect size estimates and regression analysis revealed perceived competence, peer social support and intention to be physically active (partial eta range (ηp2) .21-.25) to be the most important predictors of physical activity stage of change., Conclusions: Females in more senior years in school and in earlier physical activity stages of change reported a significantly less positive physical activity correlate profile than females in junior years and in later physical activity stages of change. This finding supports the construct validity of the physical activity stages of change.

Bovine paratuberculosis is an endemic disease caused by Mycobacterium avium subspecies paratuberculosis (Map). Map is mainly transmitted between herds through movement of infected but undetected animals. Our objective was to investigate the effect of observed herd characteristics on Map spread on a national scale in Ireland. Herd characteristics included herd size, number of breeding bulls introduced, number of animals purchased and sold, and number of herds the focal herd purchases from and sells to. We used these characteristics to classify herds in accordance with their probability of becoming infected and of spreading infection to other herds. A stochastic individual-based model was used to represent herd demography and Map infection dynamics of each dairy cattle herd in Ireland. Data on herd size and composition, as well as birth, death, and culling events were used to characterize herd demography. Herds were connected with each other through observed animal trade movements. Data consisted of 13 353 herds, with 4 494 768 dairy female animals, and 72 991 breeding bulls. We showed that the probability of an infected animal being introduced into the herd increases both with an increasing number of animals that enter a herd via trade and number of herds from which animals are sourced. Herds that both buy and sell a lot of animals pose the highest infection risk to other herds and could therefore play an important role in Map spread between herds. [ABSTRACT FROM AUTHOR]