This study aimed to investigate moderators of change in physical activity (PA) levels after 30 days (30-d) of restrictions due to the COVID-19 pandemic in young adults. This research is an extension of the CRO-PALS study and analyses for this study were performed on young adults (20–21 y.o., n = 91). Moderate-to-vigorous physical activity (MVPA), sport participation, student and socioeconomic status were assessed pre- and post-30-d restrictions. Differences in MVPA levels were examined using repeated-measures ANOVAs. After 30-d of restrictions, the drop in MVPA in females (−64.8 min/day, p = 0.006) and males was shown (−57.7 min/day, p < 0.00). However, active participants decreased, while non-active peers increased their MVPA level (−100.7 min/day, p < 0.00, and +48.9 min/day, p = 0.051, respectively). Moreover, students and non-students decreased their MVPA level (−69.0 min/day, p < 0.00, and −35.0 min/day, p = 0.22, respectively) as well as sport participants and non-sport participants (−95.3 min/day, p < 0.001, and −53.9 min/day, p < 0.00, respectively). Our results suggest that 30-d of restrictions equally affect females and males where the evident drop in MVPA is seen in both genders. However, active people decreased their PA level during lockdown and the opposite pattern was seen in non-active peers, where restrictions for them can represent an opportunity to change their behavior in a positive direction in order to gain better health status.

A systematic search of the literature was performed to compare the effects of interventions that targeted sedentary behaviours or physical activity (PA) or physical fitness on primary prevention of obesity in 6-to 12-year-old children. The search identified 146 reports that provided relevant data for meta-analysis. Point estimates in % body fat were higher for fitness interventions compared with PA interventions (standardized mean difference = −0.11%; 95% CI = −0.26 to 0.04, and −0.04%; 95% CI = −0.15 to 0.06, respectively). Including sedentary behaviour to a PA-or fitness-oriented intervention was not accompanied by an increase in intervention effectiveness, as the point estimates were slightly smaller compared with those for PA-or fitness-only interventions. Overall, the effects tended to be larger in girls than in boys, especially for PA + sedentary behaviour interventions. There was some evidence for inequality, as the effects on body mass index were seen when interventions were delivered in the general population (standardized mean difference = −0.05, 95% CI = −0.07 to −0.02), but not in groups of disadvantaged children (standardized mean difference = −0.01, 95% CI = −0.29 to 0.19). In conclusion, school-based PA interventions appear to be an effective strategy in the primary prevention of childhood obesity among 6-to 12-year-old children, but targeting sedentary behaviour in addition to PA or fitness does not increase the effectiveness of the intervention.

This study examined the association between functional movement (FM) and adiposity in adolescent population (16–17 years). This study was conducted in a representative sample of urban adolescents as the part of the CRO-PALS longitudinal study (n = 652). Body mass index (BMI), a sum of four skinfolds (S4S), waist and hip circumference were measured, and FM was assessed via Functional Movement ScreenTM (FMSTM). Furthermore, total FMSTM screen was indicator of FM with the composite score ranged from 7 to 21, with higher score indicating better FM. Multilevel analysis was employed to determine the relationship between different predictors and total FMS score. In boys, after controlling for age, moderate-to-vigorous physical activity, and socioeconomic status, total FMS score was inversely associated only with BMI (β = −0.18, p < 0.0001), S4S (β = −0.04, p < 0.0001), waist circumference (β = −0.08, p < 0.0001), and hip circumference (β = −0.09, p < 0.0001). However, among girls, in adjusted models, total FMS score was inversely associated with S4S (β = −0.03, p < 0.0001), while BMI (β = −0.05, p = 0.23), waist circumference (β = −0.04, p = 0.06), and hip circumference: (β = −0.01, p = 0.70) failed to reach statistical significance. Results showed that the association between adiposity and FM in adolescence is sex-specific, suggesting that boys with overweight and obesity could be more prone to develop dysfunctional movement patterns. Therefore, exercise interventions directed toward correcting dysfunctional movement patterns should be sex-specific, targeting more boys with overweight and obesity rather than adolescent girls with excess weight.

This study aimed to investigate sex difference in the functional movement in the adolescent period. Seven hundred and thirty adolescents (365 boys) aged 16–17 years participated in the study. The participants performed standardized Functional Movement Screen™ (FMSTM) protocol and a t-test was used to examine sex differences in the total functional movement screen score, while the chi-square test was used to determine sex differences in the proportion of dysfunctional movement and movement asymmetries within the individual FMSTM tests. Girls demonstrated higher total FMSTM score compared to boys (12.7 ± 2.3 and 12.2 ± 2.4, respectively; p = 0.0054). Sex differences were present in several individual functional movement patterns where boys demonstrated higher prevalence of dysfunctional movement compared to girls in patterns that challenge mobility and flexibility of the body (inline lunge: 32% vs. 22%, df = 1, p = 0.0009; shoulder mobility: 47% vs. 26%, df = 1, p < 0.0001; and active straight leg raise: 31% vs. 9%, df = 1, p < 0.0001), while girls underperformed in tests that have higher demands for upper-body strength and abdominal stabilization (trunk stability push-up: 81% vs. 44%, df = 1, p < 0.0001; and rotary stability: 54% vs. 44%, df = 1, p = 0.0075). Findings of this study suggest that sex dimorphisms exist in functional movement patterns in the period of mid-adolescence. The results of this research need to be considered while using FMSTM as a screening tool, as well as the reference standard for exercise intervention among the secondary school-aged population.

Karuc, J, Mišigoj-Duraković, M, Šarlija, M, Marković, G, Hadžić, V, Trošt-Bobić, T, and Sorić, M. Can injuries be predicted by functional movement screen in adolescents? The application of machine learning. J Strength Cond Res 35(4): 910–919, 2021—This study used machine learning (ML) to predict injuries among adolescents by functional movement testing. This research is a part of the CRO-PALS study conducted in a representative sample of adolescents and analyses for this study are based on nonathletic (n = 364) and athletic (n = 192) subgroups of the cohort (16–17 years). Sex, age, body mass index (BMI), body fatness, moderate-to-vigorous physical activity (MVPA), training hours per week, Functional Movement Screen (FMS), and socioeconomic status were assessed at baseline. A year later, data on injury occurrence were collected. The optimal cut-point of the total FMS score for predicting injury was calculated using receiver operating characteristic curve. These predictors were included in ML analyses with calculated metrics: area under the curve (AUC), sensitivity, specificity, and odds ratio (95% confidence interval [CI]). Receiver operating characteristic curve analyses with associated criterium of total FMS score >12 showed AUC of 0.54 (95% CI: 0.48–0.59) and 0.56 (95% CI: 0.47–0.63), for the nonathletic and athletic youth, respectively. However, in the nonathletic subgroup, ML showed that the Naïve Bayes exhibited highest AUC (0.58), whereas in the athletic group, logistic regression was demonstrated as the model with the best predictive accuracy (AUC: 0.62). In both subgroups, with given predictors: sex, age, BMI, body fat percentage, MVPA, training hours per week, socioeconomic status, and total FMS score, ML can give a more accurate prediction then FMS alone. Results indicate that nonathletic boys who have lower-body fat could be more prone to suffer from injury incidence, whereas among athletic subjects, boys who spend more time training are at a higher risk of being injured. Conclusively, total FMS cut-off scores for each subgroup did not successfully discriminate those who suffered from those who did not suffer from injury, and, therefore, our research does not support FMS as an injury prediction tool.