The COVID-19 pandemic and the social distancing implemented shortly after influence physical activity levels (PALs). The purpose of this investigation was to evaluate the changes in PAL and factors associated with PALs among Croatian adolescents while considering the impact of community (urban vs. rural living environment). The sample included 823 adolescents (mean age: 16.5 ± 2.1 years) who were tested on baseline (from October 2019 to March 2020; before COVID-19 pandemic in Croatia) and follow-up (in April 2020; during the COVID-19 pandemic and imposed rules of social distancing). Baseline testing included anthropometrics, physical fitness status, and evaluation of PALs, while follow-up included only PALs (evaluated by a standardized questionnaire through an internet application). The results showed a significant influence of the living environment on the decrease of PAL, with a larger decrease in urban adolescents. Logistic regression showed a higher likelihood for normal PALs at baseline in adolescents who had better fitness status, with no strong confounding effect of the urban/rural environment. The fitness status of urban adolescents predicted their PALs at follow-up. The differences between urban and rural adolescents with regard to the established changes in PALs and relationships between the predictors and PALs are explained by the characteristics of the living communities (lack of organized sports in rural areas), and the level of social distancing in the studied period and region/country.

Parental/familial factors are important determinants of the physical activity level (PAL) in children and adolescents, but studies rarely prospectively evaluate their relationships. This study aimed to evaluate the changes in physical activity levels among adolescents from Bosnia and Herzegovina over a two-year period and to determine parental/familial predictors of PAL in early adolescence. A total of 651 participants (50.3% females) were tested at baseline (beginning of high school education; 14 years old on average) and at follow-up (approximately 20 months later). The predictors included sociodemographic characteristics (age, gender) and parental/familial factors (socioeconomic status of the family, maternal and paternal education, conflict with parents, parental absence from home, parental questioning, and parental monitoring). Physical activity levels were evidenced by the Physical Activity Questionnaire for Adolescents (PAQ-A; criterion). Boys were more active than girls, both at baseline (t-test = 3.09, p < 0.001) and at follow-up (t-test = 3.4, p < 0.001). Physical activity level decreased over the observed two-year period (t-test = 16.89, p < 0.001), especially in boys, which is probably a consequence of drop-out from the sport in this period. Logistic regression evidenced parental education as a positive predictor of physical activity level at baseline (OR [95% CI]; 1.38 [1.15–170], 1.35 [1.10–1.65]), and at follow-up (1.35 [1.11–1.69], 1.29 [1.09–1.59], for maternal and paternal education, respectively). Parents with a higher level of education are probably more informed about the importance of physical activity on health status, and thus transfer this information to their children as well. The age from 14 to 16 years is likely a critical period for maintaining physical activity levels in boys, while further studies of a younger age are necessary to evaluate the dynamics of changes in physical activity levels for girls. For maintaining physical activity levels in adolescence, special attention should be paid to children whose parents are less educated, and to inform them of the benefits of an appropriate physical activity level and its necessity for maintaining proper health and growth.

Issues related to knowledge of nutrition and dietary supplementation (DS) are understudied in professional athletes. This study aimed to examine the possible association between knowledge of nutrition and DS (KN&DS) and dietary supplement use (DSU) among professional athletes involved in team sports. The sample comprised professional team-sport athletes (N = 912, age: 22.11 ± 3.37 years, 356 females) involved in four Olympic sports: basketball (N = 228), soccer (N = 324), volleyball (N = 154), and handball (N = 206). The participants were tested by previously validated questionnaires to examine their self-perceived competence on nutrition and DS (S/KN&DS), their objectively evaluated (tested) KN&DS (O/KN&DS), sociodemographic and sport-specific variables (predictors), and DSU (criterion). Associations between the predictors and the criterion (No-DSU - Irregular-DSU - Regular-DSU) were determined by multinomial regression analysis for the total sample and separately for the studied sports. DSU was found to be less prevalent in older and more successful players. The O/KN&DS and S/KN&DS were positively correlated with DSU, but S/KN&DS was a stronger predictor of DSU than O/KN&DS. Sport-specific associations between predictors and criterion were identified, with stronger correlations in sports with a higher prevalence of DSU. Due to the low correlations between O/KN&DS and S/KN&DS in the studied players, this study highlights the necessity for more frequent monitoring of biomarkers of nutritional status and its usage by coaches and practitioners to provide quantitative instruction.

Background: Doping behavior, including the misuse of performance-enhancing drugs, is currently a serious problem in sports, and the efficacy of preventive efforts directly depends on information regarding the associations among different precipitating factors (PF) and doping behavior. This study aimed to establish the PF of potential doping behavior (PDB) in competitive swimmers. Methods: The study included 301 swimmers from Slovenia (153 females, 16.4 ± 2.4 years), tested during the 2017 National Championship. Variables were collected by previously validated questionnaires, which included questions on sociodemographics, sports-related factors, consumption of dietary supplements, knowledge of doping, factors of hesitation, and doping-related factors (i.e., number of doping tests, opinion about the presence of doping in sport). The PDB (positive, neutral, or negative intention toward doping) was observed as a criterion, while other variables were included as predictors in multinomial regression analyses (with “negative” as reference value), which additionally controlled for gender and age category (cadet-, junior-, and senior-level) as confounders. Results: The results confirmed higher susceptibility to doping in males (positive: odds ratio (OR): 2.77, 95% confidence interval (CI): 1.27–6.04), those swimmers who reported higher alcohol consumption (neutral: OR: 2.18, 95%CI: 1.06–4.16, positive: OR: 2.14, 95%CI: 1.05–4.37), and those regularly used dietary supplements (positive: OR: 3.62, 95%CI: 1.25–10.52). Competitive achievement in Olympic- (neutral: OR: 0.57, 95%CI: 0.41–0.81, positive: OR: 0.59, 95%CI: 0.39–0.88), and non-Olympic disciplines (positive: OR: 0.54, 95%CI: 0.35–0.83) was protective against PDB. Swimmers who were more concerned about the negative social consequences of doping behavior (i.e., condemnation by family and friends) were less likely to declare a positive intention toward the PDB (family condemnation: OR: 0.69, 95%CI: 0.56–0.86, friends’ condemnation: OR: 0.65, 95%CI: 0.52–0.80). Conclusions: The results of the study provide more precise insight into the specific factors associated with PDB in swimming. The established precipitating factors should be incorporated into targeted anti-doping campaigns in this sport.

Background and Objective: Although smoking and the physical activity level (PAL) are important determinants of health status in adolescence, there is a lack of information on the relationship between smoking and PAL in early adolescence. The objective of this study was to evaluate the gender-specific relationship between smoking and PAL in 14-to-16-year-old adolescents. Materials and Methods: The sample included 650 adolescents (337 girls, 14.7 ± 0.5 years at first testing wave) from Bosnia and Herzegovina. During the first testing wave, participants were tested using structured questionnaires. Second testing was commenced after approximately 20 months (16.4 ± 0.6 years). The variables were age, gender, socioeconomic status, living environment, cigarette smoking (predictors), and PAL (criterion). Predictors were measured at the first wave, and criterion at the first-wave and second-wave. Results: For girls, smoking was negatively correlated to PAL at the first-wave (OR: 0.75, 95% CI: 0.55–0.95) and at the second-wave (OR: 0.73, 95% CI: 0.71–0.96). No significant association between smoking and PAL was found for boys. Results suggest that adolescent boys and girls do not follow the equal trajectories when it comes to relationships between smoking and PAL. Conclusions: In developing promotional public health actions related to a decrease of smoking and increase of PAL, a gender-specific approach is highly recommended. Further studies analyzing the cause–effect relationship between consumption of other types of psychoactive substances and PAL in this age group are warranted.

Stress plays a significant role in competitions and in the training of sports participants, and coaches are no exception. To better cope with stressful situations, close monitoring of coaches’ stress levels before, during, and after training and competitions is recommended. According to studies, the use of cortisol (C) and alpha-amylase (AA) as biomarkers for monitoring acute stress is recommended. Therefore, the aim of our study was to compare HR, salivary C and AA, and STAI scores before, during, and after handball matches and training sessions. The study examined one professional handball coach, aged 37, in stress markers (salivary cortisol (C) and alpha-amylase (AA) concentrations), heart rate (HR), and the State-Trait Anxiety Inventory (STAI) scores in five matches/training sessions in the First Qatar Handball League. Statistical analysis included the calculation of descriptive statistic parameters, Mann–Whitney U test for differences between match–training time points, and the effect size analysis (Cohen’s d) to calculate the magnitude of differences between match–training time points. Presented markers (C and AA) had statistically stronger reactions before, during, and after the matches than the corresponding time points of the training sessions, similar to HR data and STAI scores. Results indicate that, before and during the matches, the analyzed markers of stress increased, which might lead to the conclusion that coaches are more anxious than frightened before and during matches. Thus, stress-coping strategies for handball coaches should be more focused on stress anticipation and anxiety control.