To reduce transmission of the coronavirus, the Brazilian government adopted containment measures to control the virus’ spread, including limitations to the practice of physical activity. It was aimed to analyze the effects of COVID-19 quarantine on physical activity levels, energy expenditure, quality of life, and level of stress in a sample of the Brazilian population. The sample included 426 participants (7 to 80 years). The International Physical Activity Questionnaire, Short form survey-36 (SF-36), and Stress Perception Scale, were used to assess the level of physical activity, quality of life and stress, respectively. The anthropometric data was used to the assessment of body mass index and basal metabolic rate. Body weight increased significantly in all sample (p < 0.05). During the pandemic, 84% of the sample indicated a low level of physical activity. Furthermore, weekly energy expenditure decreased significantly in all age groups during the pandemic (children p < 0.0001; adolescents: p < 0.0001; adults p < 0.001, and elderly p < 0.0001). All aspects of quality of life, significantly reduced in both sexes in all age groups (p < 0.05). With the exception of children, stress levels increased significantly during the pandemic (adolescents: male: p = 0.003, female: p < 0.05; adults: male: p = 0.003, female: p = 0.01, and elderly: male: p = 0.02, female: p = 0.03). Findings from the present study suggests that COVID-19 social isolation has negatively affected Brazilian’ physical activity and quality of life.

Etes participated in this study (28 males and 12 females). All athletes competed in International Paralympic competitions between 2004 and 2008. According to the visual classification, 14 athletes were T/F11, 15 were T/F12, and 11 were T/F13. A standardized report form was used to collect injury data during five competitions. Results Thirty-one athletes reported 77 sports injuries, with a prevalence of 78%, a clinical incidence of 1.93 injuries per athlete, and an incidence rate of 0.39 injuries per athlete per competition. Overuse injuries accounted for 82% and traumatic injuries 18% (P < 0.05). Small variations in the prevalence and clinical incidence of injury between sexes and visual classes were observed, but these were not statistically different (P > 0.05). The highest distribution of injury was in the lower limbs (87%), followed by spine (12%) and upper limbs (1%). The body regions most affected were the thighs (33.8%), lower legs (16.9%), and knees (9.1%). The most frequent diagnoses were spasms (26%), tendinopathies (23.4%), and strains (13%). Conclusions Elite visually impaired track and field Paralympic athletes present a pattern of overuse injuries predominantly affecting the lower limbs, particularly the thighs, lower legs, and knees. These injuries are associated with tendinopathies, muscle spasms, and strains. There were no apparent differences in injury characteristics between visual classes or sex....

Background: Vertical jumps can be used to assess neuromuscular status in sports performance. This is particularly important in Cerebral Palsy Football (CP Football) because players are exposed to high injury risk, but it may be complicated because the gold standard for assessing jump performance is scarce in field evaluation. Thus, field techniques, such as mobile apps, have been proposed as an alternative method for solving this problem. Objective: This study aims to evaluate the reliability of the measures of the MyJump2 app to assess vertical jump performance in professional CP Football. Methods: We assessed 40 male CP Football athletes (age 28.1 [SD 1.4] years, weight 72.5 [SD 6.2] kg, and height 176 [SD 4.2] cm) through the countermovement jump (CMJ) and squat jump (SJ) using a contact mat. At the same time, we assessed the athletes using the MyJump2 app. Results: There were no significant differences between the instruments in SJ height (P=.12) and flight time (P=.15). Additionally, there were no significant differences between the instruments for CMJ in jump height (P=.16) and flight time (P=.13). In addition, it was observed that there were significant and strong intraclass correlations in all SJ variables varying from 0.86 to 0.89 (both P<.001), which was classified as “almost perfect.” Similar results were observed in all variables from the CMJ, varying from 0.92 to 0.96 (both P ≤.001). Conclusions: We conclude that the MyJump2 app presents high validity and reliability for measuring jump height and flight time of the SJ and CMJ in CP Football athletes.

Subjects with spinal cord injury (SCI) have been reported to present impaired left ventricular (LV) diastolic function in comparison with able-bodied (AB) ones. The present study investigated the effect of regular physical activity on the cardiac structure and function of SCI subjects. Fifty-eight SCI men (29 sedentary [SCI-S] and 29 athletes [SCI-A]) and 29 AB men were cross-sectionally evaluated by clinical, laboratory, hemodynamic, and echocardiographic analysis. All enrolled subjects were normotensive, nondiabetic, nonsmoker, and normolipemic, and the studied groups presented similar age and body mass index. SCI-S presented similar LV structural and systolic parameters but higher E/Em (8.0 ± 0.5) and lower Em/Am (1.18 ± 0.09) ratios than SCI-A and AB (E/Em = 6.4 ± 0.3 and 5.9 ± 0.3, respectively; Em/Am = 1.57 ± 0.12 and 1.63 ± 0.08, respectively; all P < 0.05 compared with SCI-S). Analysis of SCI individuals according to injury level revealed that tetraplegic athletes had similar features compared with sedentary tetraplegic subjects, except for higher Em (10.9 ± 0.6 vs 8.6 ± 0.7 cm s, P < 0.05) and lower E/Em ratio (6.3 ± 0.4 vs 8.8 ± 0.8, P < 0.05), whereas paraplegic athletes had similar features compared with sedentary paraplegic individuals, except for higher LV end-diastolic diameter (49.4 ± 1.4 vs 45.0 ± 1.0 mm, P < 0.05) and Em/Am ratio (1.69 ± 0.20 vs 1.19 ± 0.08, P < 0.05) and lower LV relative wall thickness (0.330 ± 0.012 vs 0.369 ± 0.010, P < 0.05) and heart rate (67.1 ± 4.2 vs 81.9 ± 2.8 bpm, P < 0.05). Regular physical activity is associated with improved LV diastolic function in SCI subjects and might exert distinct cardiac structural effects in tetraplegic and paraplegic subjects.

The aim of this study is comparing the accuracies of machine learning algorithms to classify data concerning healthy subjects and patients with Parkinson’s Disease (PD), toward different time window lengths and a number of features. Thirty-two healthy subjects and eighteen patients with PD took part on this study. The study obtained inertial recordings by using an accelerometer and a gyroscope assessing both hands of the subjects during hand resting state. We extracted time and temporal frequency domain features to feed seven machine learning algorithms: k-nearest-neighbors (kNN); logistic regression; support vector classifier (SVC); linear discriminant analysis; random forest; decision tree; and gaussian Naïve Bayes. The accuracy of the classifiers was compared using different numbers of extracted features (i.e., 272, 190, 136, 82, and 27) from different time window lengths (i.e., 1, 5, 10, and 15 s). The inertial recordings were characterized by oscillatory waveforms that, especially in patients with PD, peaked in a frequency range between 3 and 8 Hz. Outcomes showed that the most important features were the mean frequency, linear prediction coefficients, power ratio, power density skew, and kurtosis. We observed that accuracies calculated in the testing phase were higher than in the training phase. Comparing the testing accuracies, we found significant interactions among time window length and the type of classifier (p < 0.05). The study found significant effects on estimated accuracies, according to their type of algorithm, time window length, and their interaction. kNN presented the highest accuracy, while SVC showed the worst results. kNN feeding by features extracted from 1 and 5 s were the combination with more frequently highest accuracies. Classification using few features led to similar decision of the algorithms. Moreover, performance increased significantly according to the number of features used, reaching a plateau around 136. Finally, the results of this study suggested that kNN was the best algorithm to classify hand resting tremor in patients with PD.