Purpose of the study: The study of the reaction of the blood microcirculation system in the upper extremities during physical activity (PA) in short-track athletes of different skill levels.

Materials and methods: The study involved male short-track skaters: 5 athletes aged 14–16 years, with a sports qualification level of 3–2 ranks, 6 athletes aged 14–16 years, with a sports qualification level of 1 rank, and 6 athletes aged 16–18 years, with a sports qualification level Candidate Master of Sports (СMS). The young athletes performed a controlled anaerobic physical load on a cycle ergometer for 3 minutes at a pedal rotation rate of 60 revolutions per minute. The load was set at 3 % of body weight. For athletes with 3^rd and 2^nd ranks, the load was 1.3 ± 0.2 kg, for those with 1^st rank, it was 1.6 ± 0.4 kg, and for Candidate Master of Sports, it was 1.8 ± 0.2 kg.

Blood microcirculation was assessed by laser Doppler flowmetry. The index of microcirculation (I_m), nutritive blood flow (M_nutr), and spectral components of the LDF signal were analyzed. Changes in the asymmetry coefficient of the parameters before and after PA were evaluated.

Results: Both before and after physical activity, I_m and M_nutr were higher in the right forearm. The coefficient of asymmetry for I_m and M_nutr decreased for all groups after physical activity, while the asymmetry in the amplitude of fluctuations in regulatory mechanisms increased as the rank of athletes increased, indicating the development of local mechanisms to support hemodynamic stability in the body as skill level increased. Maximum changes were noted for I_m, M_nutr, and myogenic oscillations amplitudes, indicating high vascular reactivity and effective redistribution of blood flow to nutritive channels to provide cells with oxygen and nutrients, particularly for Candidates Master of Sports, indicating better adaptation to physical activity.

Conclusion: Obtained data confirm the existence of functional asymmetry in microcirculation, which decreases under the influence of physical activity. Athletes with a higher level of training demonstrate better adaptive abilities. These results can be used to optimize training processes by taking into account functional asymmetries.

Aim: compare the indicators of body component composition obtained using «smart scales» with the values calculated using standard formulas based on anthropometry data.

Materials and methods: 60 people (37 girls, 23 boys) were examined. The data obtained on measurements of skin fold thickness, girths and diameters, as well as information on body composition obtained using two «smart scale» models, were tabulated and further processed.

Results: when measuring body composition using two «smart scales» models and anthropometric formulas, male participants showed statistically significant differences in muscle mass (formulas — 46.67 % ± 1.06 %, OCOC — 38.80 % ± 1.46 %, PICOOC — 40.13 % ± 1.6 %; p<0.01), bone mass (formulas — 12.3 % ± 0.4 %, OKOK — 4.3 % ± 0.1 %, PICOOC — 4.37 % ± 0.13 %; p < 0.01), fat mass (formulas — 12.47 % ± 0.68 %, OKOK — 25.41 % ± 1.76 %, PICOOC — 23.17 % ± 1.74 %; p < 0.01) and the rate of basic metabolism (formulas — 1948.7 ± 48.5 kcal, OCOC — 1675.5 ± 50.8 kcal, PICOOC — 1718.2 ± 47.6 kcal; p < 0.01). There were also found significant differences in muscle mass (formulas — 50.36 % ± 0.66 %, OKOK — 39.51 % ± 1.17 %, PICOOC — 39.86 % ± 1.38 %; p < 0.01), bone mass (formulas — 9.4 % ± 0.18 %, OKOK — 5.11 % ± 0.07 %, PICOOC — 4.72 ± 0.07 %; p < 0.01) and fat mass (formulas — 15.86 ± 0.51 %, OCOC — 24.99 ± 0.99 %, PICOOC — 23.16 ± 0.89 %; p < 0.01) in women, however no statistically significant differences in the rate of basic metabolism were found (p > 0.05). There are also discrepancies in the bone mass indices of women obtained from different weight models (OCOC — 5.11 % ± 0.07 %, PICOOC — 4.72 ± 0.07 %; p < 0.05).

Conclusion: the discrepancies obtained indicate the need for further research to determine the accuracy of the methods used. Despite the availability of «smart scales» and anthropometric formulas for determining the component composition of a body, it is worthwhile combining several methods for determining the component composition of a body in order to eliminate a systematic error.