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 at clinicaltrials.gov as NCT04893746.

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.

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.