Body composition changes with age, with fat mass (FM) increasing and fat-free mass (FFM) decreasing. Higher physical activity and high or adequate protein intake are thought to be beneficial in preventing the loss of skeletal muscle mass in the elderly. We aimed to investigate the relationships between physical activity, protein intake, and FFM in older people with mild disability. Total energy expenditure (TEE) under free-living conditions was assessed using the doubly-labelled water (DLW) method, and physical activity was measured using a triaxial accelerometer. Dietary intake was assessed using a self-recorded food intake diary during the DLW period. Percent FFM was significantly positively correlated with protein intake and physical activity level (PAL) after adjustment for age and sex (protein intake r = 0.652, p < 0.001, PAL r = 0.345, p = 0.011). In multiple linear regression analysis, when PAL, moderate-to-vigorous physical activity (MVPA), or protein intake were included, 31%, 32%, and 55%, respectively, of the variation in %FFM was explained. Moreover, the addition of both PAL/MVPA and protein intake explained 61%/60%, respectively, of the variation in %FFM. Either protein intake above the currently recommended level or higher levels of physical activity would be beneficial for the maintenance of high %FFM.

Bioimpedance spectroscopy (BIS) is an easy tool to assess hydration status and body composition. However, its validity in athletes remains controversial. We investigated the validity of BIS on total body water (TBW) and body composition estimation in Japanese wrestlers and untrained subjects. TBW of 49 young Japanese male subjects (31 untrained, 18 wrestlers) were assessed using the deuterium dilution method (DDM) and BIS. De Lorenzo’s and Moissl’s equations were employed in BIS for TBW estimation. To evaluate body composition, Siri’s 3-compartment model and published TBW/fat-free mass (FFM) ratio were applied in DDM and BIS, respectively. In untrained subjects, DDM and BIS with de Lorenzo’s equation showed consistent TBW estimates, whereas BIS with Moissl’s equation overestimated TBW (p < 0.001 vs. DDM). DDM and BIS with de Lorenzo’s equation estimated FFM and percent of fat mass consistently, whereas BIS with Moissl’s equation over-estimated and under-estimated them (p < 0.001 vs. DDM). In wrestlers, BIS with de Lorenzo’s and Moissl’s equations assessed TBW similarly with DDM. However, the Bland–Altman analysis revealed a proportional bias for TBW in BIS with de Lorenzo’s equation (r = 0.735, p < 0.001). Body composition assessed with BIS using both equations and DDM were not different. In conclusion, BIS with de Lorenzo’s equation accurately estimates the TBW and body composition in untrained subjects, whereas BIS with Moissl’s equation is more valid in wrestlers. Our results demonstrated the usefulness of BIS for assessing TBW and body composition in Japanese male wrestlers.

We aimed to investigate the effects of a wide range of daily physical activity (PA) levels on energy balance (EB), energy intake (EI), and appetite. Nine young men completed three different PA levels in a metabolic chamber in a random order: (1) no exercise (Low-PA); (2) 25 min walking seven times (Mid-PA); and (3) 25 min running seven times (High-PA) within a 24 h period. Interval exercise (25 min exercise and 35 min rest) was performed three times in the morning and four times in the afternoon. The exercise intensities were 21.6% and 53.7% $\dot{\mathrm{V}}$ O₂ peak for the Mid-PA and High-PA days, respectively. Participants were served three standardized meals and a buffet for dinner. The 24 h EB was calculated as 24 h energy expenditure (EE) minus 24 h EI. The 24 h EEs for the Low-PA, Mid-PA, and High-PA days were 1907 ± 200, 2232 ± 240, and 3224 ± 426 kcal, respectively, with significant differences observed among the three conditions (p < 0.01 for Low-PA vs. Mid-PA, Low-PA vs. High-PA, and Mid-PA vs. High-PA, respectively). The 24 h EIs for the Low-PA, Mid-PA, and High-PA days were 3232 ± 528, 2991 ± 617, and 3337 ± 684 kcal, and were unaffected by PA levels (p = 0.115). The 24 h EBs were 1324 ± 441 kcal (Low-PA), 759 ± 543 kcal (Mid-PA), and 113 ± 430 kcal (High-PA), with significant differences observed between Low-PA vs. Mid-PA (p = 0.0496), Low-PA vs. High-PA (p ≤ 0.01), and Mid-PA vs. High-PA (p = 0.017) conditions. The EB in the Low-PA group was the highest of the three conditions. Appetite perception did not differ among the study days, however there was an interaction trend (p = 0.078, time × condition). Thus, significantly different daily PA did not affect 24 h EI, however markedly affected 24 h EB, implying that EB is not automatically matched during a single day.

Jumping performance is influenced by body composition and excess fat mass impairs performance. Maintaining optimal fat mass and fat‐free mass (FFM) is crucial for enhancing jump height. However, there is limited evidence on short‐term weight loss programs that reduce fat mass without water restriction and their effects on muscle function and jumping performance. This study aimed to clarify the effects of a 1‐week weight loss program on jumping height and muscle function of volleyball players. The weight loss group engaged in two 40 min slow‐paced jogging sessions in addition to their daily training routine. Energy intake was restricted without limitations on water intake. Total body water and body composition using the deuterium dilution method, muscle strength, and jump height before and after 1 week were evaluated for those in the weight loss and control groups. Body mass was significantly reduced in the weight loss group (−2.7 ± 1.3%, p < 0.05) with a significant reduction in fat mass (−17.7 ± 10.7%, p < 0.05). Meanwhile, there were no significant changes in total body water or FFM. Muscle strength and power tests indicated no significant differences between the groups; no notable differences were observed in handgrip strength or knee extension torque. The height of a single vertical and continuous jump remained consistent pre‐ and postintervention in the control group. In the weight loss group, although the height of a single vertical jump exhibited a slight decline postintervention, the height of a continuous jump displayed no significant changes. The short‐term weight loss program significantly reduced fat mass without compromising muscle function, which is crucial for sports performance. These findings may benefit other athletes who require fat mass reduction while maintaining muscle function and help create new programs during specific training phases.

Fat‐free mass (FFM) is a key component of body composition that contributes moderately to water turnover (WT). However, FFM consists of multiple components, and the extent of its contribution to WT, especially in relation to skeletal muscle mass (SMM) in athletes, remains unclear. This study aimed to examine the association between WT and body composition in athletes. Thirty male participants (20 ± 2 years old) were members of college sports clubs or athletic organizations. WT was determined using the deuterium dilution method for 1 week. Total body water (TBW), intracellular water (ICW), and extracellular water (ECW) were measured using deuterium and sodium bromide dilution. Bone mineral content was measured using DXA, and four‐component models were used to calculate FFM and total body protein (TBP). MRI and MRS were used to evaluate SMM and muscle glycogen. WT was positively associated with TBW ( r = 0.563), ICW ( r = 0.549), SMM ( r = 0.548), and ambient temperature ( r = 0.519). To overcome mathematical coupling and multicollinearity, regularized regression analyses (LASSO and Elastic Net) were performed. Because TBW and TBP are algebraically dependent on the deuterium‐derived water space from which WT was calculated, they were excluded from the candidate predictors. Both models retained SMM, ICW, and ambient temperature as predictors of WT, whereas ECW, bone mineral content, muscle glycogen, and relative humidity were not retained. The modest cross‐validated performance (leave‐one‐out R ² = 0.20–0.23) suggests that WT in athletes is also influenced by behavioral and unmeasured environmental factors. Functional body composition compartments, specifically SMM and ICW, were associated with WT in athletes beyond the mathematical dependency between WT and TBW; SMM, measured independently by MRI, was an independent correlate, whereas the association of ICW—derived in part from TBW—was not fully independent of the WT measurement.