درخت حوزه‌های تخصصی

Background: The immune properties of mothers’ milk are well known. But the effects of physical activity on humeral immune properties of mothers’ milk is still undetermined. Therefore, reports on the impact of exercise on IgA concentrations of mothers’ milk is controversial. The purpose of the present study was the investigation of the effects of selected aerobic exercises in maximum intensity of 60-70% of heart rate reserve on IgA concentrations in breast milk as well as body composition. Method: In this research, 28 sedentary women (29±5.7 years; VO2max 36±4 ml/Kg/min), divided into two exercise and control groups through random sampling, took part. The exercise group performed some particular exercises for 10 weeks whereas the control group did not do so. Milk samples were taken from both groups and measured by the ELISA method. Body composition was also measured in different stages of the study. Results: In the rest status, the two groups were identical in terms of IgA concentrations (p= 0.549) and body composition (p=0.204). IgA concentrations under exercise load of 60% (p=0.060) and 70% (p= 0.001) of the HRR respectively showed a significant increase as compared to the resting status mean values in the two groups. Body composition variables were only of significant values in terms of comparing the general effects, only in the factors of group (p=0.003) and reciprocal effect of the grouping - the duration of the exercises (p=0.024). Conclusions: The results of this research show that, under the effects of moderate intensity exercise, the mothers’ secretory immune system experiences some changes. Therefore, IgA concentrations in the milk increase. In addition, with the reduction of fat weight, the decrease of fat mass percentage, and increase in the body density, the level of the mothers’ aerobic fitness is increased, which is in no contradiction with their lactation performance.

The current study was conducted to identify the impact of artificial intelligence on sports marketing (applications and limitations). This research is a sequential mixed-methods study (qualitative-quantitative). The participants in qualitative phase consisted of 15 sport management experts who were interviewed. In the quantitative section, the statistical population included members of the sports management academic community that was selected through random sampling approach. Kolmogorov-Smirnov test, confirmatory factor analysis, and path analysis were used in the quantitative part of the study. The structural equation method with partial least squares method was used to measure the validity of the model, and the research hypotheses. According to the results of the study, there are 8 effective factors of artificial intelligence applications in sports marketing, including predicting sales and prices of sports products/services, identifying revenue growth opportunities, automating sales processes of sports products/services, personalized recommendation systems through analyzing customer behavior of sports products/services, tracking satisfaction with sports products/services, and advertising. Additionally, there are 5 factors identified as limitations of artificial intelligence in sports marketing, including limited understanding of human elements, concerns about data privacy, costs, and technical issues. The findings can help sports marketers to better utilize artificial intelligence for better delivery and customer satisfaction of sports products and services.