Feasibility of predicting maximal oxygen uptake by using the efficiency factor in healthy men.

Conventionally, efficiency is indirectly estimated through a respiratory gas analyser (oxygen, carbon dioxide), which is a complex and rather costly calculation method that is difficult to perform in many situations. Therefore, the present study proposed a modified definition of efficiency, called the efficiency factor (EF) (i.e., the ratio of work to the corresponding exercise intensity), and evaluated the relation between the EF and maximal oxygen uptake ([Formula: see text]), as well as compared the prediction models established based on the EF. The heart rate (maximal heart rate: 186 ± 6 beats min-1), rating of perceived exertion (19 ± 1), and [Formula: see text] (39.0 ± 7.1 mL kg-1 min-1) of 150 healthy men (age: 20 ± 2 years; height: 175.0 ± 6.0 cm; weight: 73.6 ± 10.7 kg; body mass index [BMI]: 24.0 ± 3.0 kg m-2; percent body fat [PBF]: 17.0 ± 5.7%) were measured during the cardiopulmonary exercise test (CPET). Through multiple linear regression analysis, we established the BMI model using age and BMI as parameters. Additionally, we created the PBF modelHRR utilizing weight, PBF, and heart rate reserve (HRR) and developed PBF modelEF6 and PBF modelEF7 by incorporating EF6 from the exercise stage 6 and EF7 from the exercise stage 7 during the CPET, respectively. EF6 (r = 0.32, p = 0.001) and EF7 (r = 0.31, p = 0.002) were significantly related to [Formula: see text]. Among the models, the PBF modelEF6 showed the highest accuracy, which could explain 62.6% of the variance in the [Formula: see text] at with a standard error of estimate (SEE) of 4.39 mL kg-1 min-1 (%SEE = 11.25%, p < 0.001). These results indicated that the EF is a significant predictor of [Formula: see text], and compared to the other models, the PBF modelEF6 is the best model for estimating [Formula: see text].

Lactobacillus plantarum TWK10 Improves Muscle Mass and Functional Performance in Frail Older Adults: A Randomized, Double-Blind Clinical Trial.

Sarcopenia is a condition in which there is a loss of muscle caused by aging and it is one of the most significant factors that affects physical fragility. In recent years, the role of the gut-muscle axis has garnered attention as, along with the gut microbiota, it potentially plays a significant role in muscle regeneration, in addition to nutritional supplements and exercise training. Past studies have found that supplementation with Lactobacillus plantarum TWK10 could effectively increase the muscle mass of animals or adult humans. Therefore, in this study, we investigated whether the supplementation of L. plantarum TWK10 produces increased muscle mass and improves the functional performance of elderly persons with mild fragility. A total of 68 elderly subjects were recruited, of which 13 subjects were excluded or withdrew from the study. We adopted a double-blind design, and the 55 subjects were randomly divided into three groups: the placebo group, the TWK10 low-dose group (2 × 1010 CFU/day) (TWK10-L), and the TWK10 high-dose group (6 × 1010 colony-forming unit (CFU)/day) (TWK10-H). For 18 weeks, all subjects were required to regularly take experimental samples, perform functional activity testing, and have their body composition analyzed before the study and every six weeks after the intervention. Finally, 17 subjects in the placebo group, 12 subjects in the TWK10-L group, and 13 subjects in the TWK10-H group finished the study. It was found that supplementation with TWK10 had a tendency to increase and improve muscle mass, left hand grip strength, lower limb muscle strength, and gait speed and balance after the sixth week, especially in the TWK10-H group, and, as the supplement time was longer up to the 18th week, it had an even greater effect (p < 0.05). In conclusion, consecutive supplementation of L. plantarum TWK10 for more than six weeks could effectively improve the muscle strength and endurance of the elderly, reducing sarcopenia and physical fragility. This trial was registered as NCT04893746.

Feasibility of the Energy Expenditure Prediction for Athletes and Non-Athletes from Ankle-Mounted Accelerometer and Heart Rate Monitor.

Due to the nature of micro-electromechanical systems, the vector magnitude (VM) activity of accelerometers varies depending on the wearing position and does not identify different levels of physical fitness. Without an appropriate energy expenditure (EE) estimation equation, bias can occur in the estimated values. We aimed to amend the EE estimation equation using heart rate reserve (HRR) parameters as the correction factor, which could be applied to athletes and non-athletes who primarily use ankle-mounted devices. Indirect calorimetry was used as the criterion measure with an accelerometer (ankle-mounted) equipped with a heart rate monitor to synchronously measure the EE of 120 healthy adults on a treadmill in four groups. Compared with ankle-mounted accelerometer outputs, when the traditional equation was modified using linear regression by combining VM with body weight and/or HRR parameters (modified models: Model A, without HRR; Model B, with HRR), both Model A (r: 0.931 to 0.972; ICC: 0.913 to 0.954) and Model B (r: 0.933 to 0.975; ICC: 0.930 to 0.959) showed the valid and reliable predictive ability for the four groups. With respect to the simplest and most reasonable mode, Model A seems to be a good choice for predicting EE when using an ankle-mounted device.

Reliability and validity of the physical activity monitor for assessing energy expenditures in sedentary, regularly exercising, non-endurance athlete, and endurance athlete adults.

BACKGROUND: Inertial sensors, such as accelerometers, serve as convenient devices to predict the energy expenditures (EEs) during physical activities by a predictive equation. Although the accuracy of estimate EEs especially matter to athletes receive physical training, most EE predictive equations adopted in accelerometers are based on the general population, not athletes. This study included the heart rate reserve (HRR) as a compensatory parameter for physical intensity and derived new equations customized for sedentary, regularly exercising, non-endurance athlete, and endurance athlete adults. METHODS: With indirect calorimetry as the criterion measure (CM), the EEs of participants on a treadmill were measured, and vector magnitudes (VM), as well as HRR, were simultaneously recorded by a waist-worn accelerometer with a heart rate monitor. Participants comprised a sedentary group (SG), an exercise-habit group (EHG), a non-endurance group (NEG), and an endurance group (EG), with 30 adults in each group. RESULTS: EE predictive equations were revised using linear regression with cross-validation on VM, HRR, and body mass (BM). The modified model demonstrates valid and reliable predictions across four populations (Pearson correlation coefficient, r: 0.922 to 0.932; intraclass correlation coefficient, ICC: 0.919 to 0.930). CONCLUSION: Using accelerometers with a heart rate monitorcan accurately predict EEs of athletes and non-athletes with an optimized predictive equation integrating the VM, HRR, and BM parameters.