Monitoring the training load during training is important for quantifying the demand on psychological and physiological responses. This procedure is achieved through subjective and objective methods applied to the control of the level of training, to the attainment of conditioning and performance goals, and to the prevention of injuries. Training load refers to either external load, such as the variables of speed, distance, accelerations, and decelerations, or internal load, which is related to the psychological and physiological responses during an exercise session or training period (e.g., rating of perceived exertion—RPE; and heart rate—HR). To measure external load, traditional methods include pedometers, accelerometers, global positioning systems, and volume load. For internal load, methods include RPE, training monotony, strain, and impulse, HR, hormonal and biochemical markers, and training diaries and questionnaires. The current review reinforces the assumption that the methods should be combined to improve confidence with the information, mainly when assessing internal load stress during training. Moreover, training load provides an objective assessment of performance levels and involvement in different training phases, thus providing relevant information to analyse strategies for the effectiveness of conditioning progress, performance enhancement, and injury prevention.

Citrus flavonoids, such as hesperidin, have shown therapeutic properties that improve hyperglycemia and insulin resistance, and decrease blood serum lipids and inflammation. The current investigation studied the effects of hesperidin supplementation associated with continuous and interval swimming on the biochemical parameters (glucose, cholesterol and triglycerides), and oxidative stress markers (TBARS and DPPH) in rats.

Objective: To analyze the association between social isolation (SI), physical activity level (PAL) and sedentary behavior in the university community in pandemic times. Methods: A cross-sectional epidemiological study was carried out from May 7 to June 4, 2020, with 194 participants linked to the Federal University of Jataí (Universidade Federal de Jataí – UFJ), in Goiás, Brazil. Data were collected using a form created on Google Forms® and sent to the email addresses of the academic community of UFJ to assess socioeconomic characteristics, lifestyle, body composition, physical activity level, and sedentary behavior, taking into account the periods prior to and during SI. Data were analyzed using descriptive and inferential statistics, with p

The challenge in the search for relationships between urban space, physical mobility, and health status, is detecting indicators able to link the environment with healthy life habits. Therefore, the objective was to design an urban index for the identification of urban environment propensity for physical activity (PA) and to determine how it relates to lifestyle and anthropometric parametrization of obesity. Participants (N = 318–60.4% women and 39.6% men) were recruited from a mid-sized city with epidemiology and morbidity rates below the average for the mid-west region of Brazil. Body mass index (BMI) was measured and a questionnaire was applied to gather information about PA and life habits. The spatial urban health index (SUHI) was designed in a geographic information system using data from demographic, environmental and urban physical features. The relationship between BMI and PA was verified with multiple linear regression, controlled for SUHI levels. Regarding the BMI of the population, 69.5% were classified in the eutrophic or overweight ranges, with no effect of gender and age. The SUHI classified 63.7% of the urban area favorable to PA. The PA routine was adequate (≥3 sessions with ≥1 h each) for ~80% of the population, as well as healthy habits such as non smoking (~94%) and non alcohol abuse (~55%). The SUHI strengthens the relationships of BMI to weekly frequency (r = -0.68; t = -9.4; p<0.001) and session duration (r = -0.66; t = -2.8; p<0.001) for the whole group by improving the explanatory coefficient in ~25% (R²_Adj = 0.61 to R²_Adj = 0.85). The SUHI indicated that the urban environment is able to promote healthy life habits by diminishing the “obesogenic” features of the city when physical structures are planned to facilitate PA, whatever the gender and age group.