The purpose of this study is to investigate changes in gut microbiota related to metabolic diseases after moderate and high-intensity exercise. A total of 24 participants were divided into three groups: Non-Exercise Group (NEG, n = 8, 28.6 ± 5.3 years, 176.0 ± 7.8 cm, 81.3 ± 14.6 kg), Moderate Intensity Exercise Group (MIEG, n = 8, 26.5 ± 3.3 years, 176.9 ± 5.0 cm, 75.4 ± 9.5 kg), and Vigorous Intensity Exercise Group (VIEG, n = 8, 30.6 ± 5.9 years, 174.2 ± 3.5 cm, 77.8 ± 12.2 kg). The participants were selected by assessing physical activity, gut health status, presence of diseases, recent disease diagnoses, and dietary disorders. Those who reported any presence disease or recent disease diagnosis were excluded from the current study. Stool samples were collected after a 10-h fast for gut microbiome analysis. MIEG participants trained at 40–59 % heart rate reserve (HRR) for at least 150 min per week, while VIEG participants trained at ≥ 60 % HRR for at least 90 min per week. After 4 weeks, all participants provided stool samples for gut microbiome analysis. Data analysis was conducted using the Wilcoxon test, with statistical significance set at ≤ 0.05. The results indicated an increase in Prevotella in MIEG, while Veillonella, Dorea_formicigenerans, and Dorea_longicatena exhibited a decrease (p < 0.05). In VIEG, there was an increase in Bacteroides, Butyricimonas, Odoribacter, and Alistipes (p < 0.05). These modified microbial groups were associated with factors related to metabolic diseases, including inflammatory bowel disease, obesity, colorectal cancer, diabetes, hypertension, metabolic liver diseases, and ischemic heart diseases. Additional research is essential to delve into the relationship between exercise and these alterations in the microbiome.

This study developed and validated simplified, individualized heart rate (HR)-based regression models to predict energy expenditure (EE) during treadmill exercise without direct VO2 calibration, addressing the need for more practical and accurate methods that overcome limitations of existing predictions and facilitate precise EE estimation outside specialized laboratory conditions. Energy expenditure was measured by assessing oxygen uptake (VO₂) using a portable gas analyzer and predicted across three treadmill protocols: Bruce, Modified Bruce, and Progressive Speed. These protocols were selected to capture a wide range of exercise intensities and improve the accuracy of heart rate-based EE predictions. The six models combined heart rate, heart rate reserve (HRres), and demographic variables (sex, age, BMI, resting HR) using the Enter method of multiple regression, where all variables were included simultaneously to enhance the real-world applicability of the energy expenditure predictions. All models showed high accuracy with R² values between 0.80 and 0.89, and there were no significant differences between measured and predicted energy expenditure (p ≥ 0.05). HRres-based models outperformed others at submaximal intensities and remained consistent across sex, weight, BMI, and resting HR variations. By incorporating individual resting and maximal HR values, HRres models offer a personalized, physiologically relevant estimation method. These results support integrating HRres-based EE prediction into wearable devices to improve accessible and precise monitoring of physiological energy metabolism.

Backgrounds/Objectives: The purpose of this study was to compare blood flow and exercise performance in high-temperature environments with Kinesio taping. Methods: Ten heathy men performed a Bruce treadmill test until volitional fatigue in a heated environment (35 °C, 50% relative humidity), with (KTC) and without Kinesio taping (UTC). Blood flow (BF), maximal oxygen uptake (VO₂max), heart rate (HR), and blood lactate (BL) were measured at baseline, after 30 min of acclimation, during treadmill exercise, and after 30 min of recovery. Results: Baseline and resting measurements of BF, VO₂max, HR, and BL were similar between conditions (p > 0.05). The results revealed no significant differences in any variables between conditions (p > 0.05), except for BF. BF was significantly higher in the KTC compared to the UTC during exercise (p < 0.05). VO₂, HR, and BL significantly increased during exercise, immediately post-exercise, 10 min post-exercise, and 30 min post-exercise compared to baseline in both KTC and UTC conditions (all p < 0.05). When Kinesio tape was applied, BF significantly increased during and after exercise. However, VO₂, HR, and BL did not differ between conditions. Conclusions: These results showed no evidence that Kinesio tape improves exercise performance or recovery in heat conditions.

OBJECTIVES This study examines the impact of blood flow restriction (BFR) resistance exercise on blood flow across varying environmental temperatures, aiming to understand the physiological responses, exercise function, and recovery process affected by temperature changes.METHODS Seven healthy males (age: 26.4±1.7 years, weight: 72.7±6.8 kg, height: 174.5±2.0 cm) participated in biceps brachii exercises with and without BFR under low (10°C/25% humidity), moderate (25°C/50% humidity), and high (40°C/50% humidity) environmental conditions. Each participant completed six sessions of exercise across these conditions to assess the recovery phase blood flow changes. Blood flow measurements were taken during the recovery phase for analysis.RESULTS Blood flow significantly increased in high-temperature conditions, independent of BFR application, showing notable differences compared to low-temperature conditions (p<.05).CONCLUSIONS Environmental temperature significantly affects blood flow changes during BFR resistance exercise, with increased blood flow in high temperatures indicating a physiological adaptation to thermal stress. This suggests the need to consider environmental temperatures in optimizing BFR exercise protocols.