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Skeletal muscle experiences both quantitative and qualitative changes with aging. Echo intensity (EI) of the skeletal muscle obtained by ultrasonography (US) has been considered to reflect muscle quality. This technique is noninvasive, easily accessible, and is relatively inexpensive than that of other imaging techniques such as computed tomography (CT). Previous studies have reported that EI is related to several physical performances. However, few studies have investigated the validity of EI against other imaging methods in case of imaging of the skeletal muscle. We compared quantitative and qualitative indices evaluated by the US and CT imaging systems to strengthen their validity. A total of 40 adults participated in this study: 19 young (10 men and 9 women; their mean (±SD) age was 22.7±1.5 years) and 21 older adults (13 men and 8 women; their mean age was 70.6±4.8 years). Both thighs of each participant were evaluated using US and CT imaging systems. With respect to the US indices, the young group had significantly higher muscle thickness (MT) of the front thigh ( <0.001) and lower EI ( =0.001) than that of the older group. With respect to the CT indices, the cross-sectional area (CSA) and mean CT value (Hounsfield unit [HU]) of the muscle were found to be significantly higher in the young group ( <0.001) than that of the older group, whereas the percentage of low-density muscle area (%LDMA) was found to be significantly higher in the older group ( <0.001) than that of the young group. A significant, strong, and positive correlation was observed between MT and CSA, which reflects the muscle quantity ( =0.774; <0.001). With respect to the indices of muscle quality, we found significant and moderate correlations between EI and CT values ( =-0.502; <0.001) and between EI and %LDMA ( =0.441; <0.001). However, these correlations were found to be decreased in the older group [between EI and CT value ( =-0.363; =0.018) and between EI and %LDMA ( =0.257; =0.100)]. Results of this study indicate that the EI is moderately associated with muscle attenuation as assessed by CT, which means that higher EI at least partly reflects intramuscular lipid infiltration.

The current study examined how body weight and lifestyle fluctuate between spring, autumn, and winter in Japanese female college students and whether weight gain is associated with changes in physical activity, food intake, and sleep. We measured body weight and lifestyle factors in 31 participants from May 2017 to January 2018. Weight was measured daily in participants’ homes. Physical activity and sleep were measured for three weeks in three seasons using two accelerometers. Food intake was assessed using a validated food frequency questionnaire. Body weight significantly decreased in autumn compared with spring (p < 0.001). Body weight in winter tended to increase compared with autumn (p = 0.052). Step counts and energy intake were significantly different between seasons (p < 0.05). Total time in bed was not significantly different between seasons. In comparisons of changes in lifestyle patterns from autumn to winter between the weight gain (≥0.5 kg) and weight maintenance groups, seasonal changes in lifestyle factors were not significantly different between groups (p > 0.05). The results indicated that body weight and lifestyle were affected by seasonal variability in female college students, but no significant relationships existed between seasonal weight gain and changes in lifestyle patterns.

Aim: To examine whether mild early time-restricted eating (eating dinner at 18:00 vs. at 21:00) improves 24-h blood glucose levels and postprandial lipid metabolism in healthy adults. Methods: Twelve participants (2 males and 10 females) were included in the study. In this 3-day (until the morning of day 3) randomized crossover study, two different conditions were tested: eating a late dinner (at 21:00) or an early dinner (at 18:00). During the experimental period, blood glucose levels were evaluated by each participant wearing a continuous blood glucose measuring device. Metabolic measurements were performed using the indirect calorimetry method on the morning of day 3. The study was conducted over three days; day 1 was excluded from the analysis to adjust for the effects of the previous day’s meal, and only data from the mornings of days 2 and 3 were used for the analysis. Results: Significant differences were observed in mean 24-h blood glucose levels on day 2 between the two groups (p = 0.034). There was a significant decrease in the postprandial respiratory quotient 30 min and 60 min after breakfast on day 3 in the early dinner group compared with the late dinner group (p < 0.05). Conclusion: Despite a difference of only 3 h, eating dinner early (at 18:00) has a positive effect on blood glucose level fluctuation and substrate oxidation compared with eating dinner late (at 21:00).

Breakfast skipping has become an increasing trend in the modern lifestyle and may play a role in obesity and type 2 diabetes. In our previous studies in healthy young individuals, a single incident of breakfast skipping increased the overall 24-h blood glucose and elevated the postprandial glycaemic response after lunch; however, it was difficult to determine whether this response was due to breakfast omission or the extra energy (i.e. lunch plus breakfast contents). The present study aimed to assess the postprandial glycaemic response and to measure their hormone levels when healthy young individuals had identical lunch and dinner, and the 24-h average blood glucose as a secondary outcome. Nine healthy young men (19−24 years) participated in two-meal trials: with breakfast (three-meal condition) or without breakfast (breakfast skipping condition). During the meals, each individual’s blood glucose was continuously monitored. Skipping breakfast resulted in a significantly higher (P < 0·001) glycaemic response after lunch as compared with the glycaemic response after an identical lunch when breakfast was consumed. Despite the difference in the total energy intake, the 24-h average blood glucose was similar between the two-meal conditions (P = 0·179). Plasma NEFA level was significantly higher (P < 0·05) after lunch when breakfast was omitted, and NEFA level positively correlated with the postprandial glycaemic response (r 0·631, P < 0·01). In conclusion, a single incident of breakfast skipping increases postprandial hyperglycaemia, and associated impaired insulin response, after lunch. The present study showed that skipping breakfast influences glucose regulation even in healthy young individuals.

Previous cross-sectional studies have indicated that low relative appendicular lean mass (ALM) against body weight (divided by body weight, ALM/Wt, or divided by body mass index, ALM/BMI) was negatively associated with metabolic syndrome (MetS). Conversely, previous cross-sectional studies have indicated that the absolute ALM or ALM divided by squared height (ALM/Ht 2 ) were positively associated with MetS. The aim of this longitudinal study was to investigate the association between low absolute or relative skeletal muscle mass, leg muscle power, or percent body fat and the development of MetS in Japanese women in a 7-y prospective study. The study participants included 346 Japanese women aged 26 to 85 years. The participants were divided into low and high groups based on the median values of ALM/Wt, ALM/BMI, ALM/Ht 2 , absolute ALM, or leg power. The longitudinal relationship between ALM indices or leg power and MetS development was examined using Kaplan-Meier curves and Cox regression models (average follow-up duration 7 years, range 1 to 10 years). During follow-up, 24 participants developed MetS. MetS incidence was higher in the low ALM/Wt group than the high ALM/Wt group even after controlling for age, obesity, waist circumference, family history of diabetes, smoking, and physical activity [adjusted hazard ratio = 5.60 (95% CI; 1.04–30.0)]. In contrast, MetS incidence was lower in the low ALM/Ht 2 group than the high ALM/Ht 2 group [adjusted hazard ratio = 10.6 (95%CI; 1.27–89.1)]. MetS incidence was not significantly different between the low and high ALM/BMI, absolute ALM, and leg power groups. Both ALM/Ht 2 and ALM/Wt were not significant predictive variables for MetS development when fat mass or percent body fat was taken into account in the Cox model. At the very least, the results of this study underscore the importance of body composition measurements in that percent body fat, but not ALM, is associated with MetS development.

Background: In Japan, the incidence of kidney stones has increased markedly in recent decades. Major causes of kidney stones remain unclear, and limited data are available on the relationship between overweight/obesity and the incidence of kidney stones. We therefore evaluated body mass index (BMI) and the incidence of kidney stones in Japanese men. Methods: Of the workers at a gas company, 5984 males aged 20-40 years underwent a medical examination in 1985 (baseline). This study includes 4074 of the men, who were free of kidney stones at baseline and underwent a second medical examination performed between April 2004 and March 2005. BMI was calculated from measured height and weight in 1985, and men were categorized into tertiles. The development of kidney stones during follow-up was based on self-reports from questionnaires at the second medical examination. Results: The average duration of follow-up was 19 years, with 258 participants developing kidney stones during this period. Using the lowest BMI (1st tertile) group as a reference, the hazard ratios (95% confidence intervals [CIs]) for the 2nd and 3rd BMI tertiles were: 1.26 (95% CI, 0.92-1.73) and 1.44 (95% CI, 1.06-1.96), respectively (P for trend = 0.019). After additionally adjusting for potential confounders, such as age, systolic blood pressure, cardiorespiratory fitness, cigarette smoking, and alcohol consumption, the hazard ratios were 1.28 (95% CI, 0.93-1.76) and 1.41 (95% CI, 1.02-1.97), respectively (P for trend = 0.041). Conclusions: These results suggest that increased BMI is a risk factor for kidney stones in Japanese men.

The aim of the present study was to determine the total energy expenditure, physical activity and dietary intake of men with type 2 diabetes mellitus and control participants without type 2 diabetes mellitus who were matched for age and body mass index. The participants in the present study were 12 well‐controlled type 2 diabetes mellitus patients and 10 controls, aged 40–75 years, with a body mass index <30 kg/m2. Total energy expenditure under free‐living conditions was assessed using the doubly labeled water method, and physical activity was measured using a triaxial accelerometer. Dietary intake was assessed using a self‐recorded food intake diary during the measurement period. Participants were instructed to record their dietary intake over 3 days, including 2 weekdays. Total energy expenditure was not significantly different between the groups (P = 0.153), nor were energy (P = 0.969) or macronutrient intakes. In conclusion, when age and body mass index are matched, total energy expenditure and self‐reported energy intake are not significantly different between type 2 diabetes mellitus patients and healthy controls. The aim of this study was to investigate differences in total energy expenditure using doubly‐labeled water, physical activity by accelerometer, and reported dietary intake between patients with type 2 diabetes mellitus and those without type 2 diabetes mellitus. Total energy expenditures, physical activity, and energy intakes were not significantly different between T2DM patients and healthy controls.

The escalating effects of climate change, particularly global warming, are posing an increasing burden on human health. Older adults are particularly susceptible to the impact of extreme heat. Adequate water intake is essential to prevent dehydration in hot environments. Therefore, it is important to understand water turnover (WT) and intake. WT of older adults in hot environments remains unknown. This study aimed at investigating the seasonal effects on WT, total energy expenditure (TEE), and physical activity using doubly labeled water (DLW) and a triaxial accelerometer. A total of 26 older Japanese adult males and females aged ≥ 65 years participated in the study. WT and TEE were measured using DLW in May and August 2012. The mean values of maximum, mean, and minimum temperatures and mean humidity of the measurement days were 24 °C, 19 °C, 14 °C, and 57% in May (spring) and 35 °C, 29 °C, 25 °C, and 66% in August (summer) 2012, respectively. The mean (standard deviation, SD) age of the participants was 73.7 (5.4) years. Total body water increased significantly from 31.1 (4.6) to 31.9 (5.2) kg (+ 0.8 kg, P  = 0.009) from May to August. TEE decreased significantly from 2271 (280) to 2123 (470) kcal/day (- 149 kcal/d, P  = 0.036), while WT increased significantly from 2.939 (0.625) to 3.579 (0.943) L/day (+ 0.640 L/d, P  < 0.001). WT increased by 640 mL/day during summer compared to that during spring, when the average temperature was 19 °C. Our findings indicate that WT increases during hot weather in older adults, reflecting seasonal adaptation.

Reports that collect and organize dietary reference intake (DRI) data for military operations in different countries and regions worldwide are limited.This scoping review aimed to collect and organize information on the status of formulating a DRI for military operations in each country. For the information search, we queried PubMed and Google for literature and reports on the DRI for military operations and summarized the content of the adopted literature and reports. The content and rationale for DRI for military operations in Australia, the United Kingdom (UK), the United States of America (USA), and the North Atlantic Treaty Organization (NATO) can be summarized as follows: (1) Energy requirements: Four reports formulated physical activity levels (PALs) and corresponding energy requirements that differed from those for the civilian public. The PAL range for the military was set as high as 1.50-3.20, as opposed to the standard civilian upper PAL set at 1.20-2.20. (2) Protein: Three military reports outside of the UK had different standards than those for the civilian public with an increased intake in accordance with the high PAL while simultaneously preventing excessive intake.In the military, values were formulated 1.2-4.8 times higher than the standards for civilians (45-65 g/day to 55-307 g/day). (3) Macronutrient energy distribution: Four military reports established macronutrient energy distributions that differed from those for the civilian public. The DRI for the Australian and UK militaries was formulated such that as PAL increased, protein decreased, fat decreased or remained unchanged, and carbohydrate increased. (4) Sodium: Considering that military personnel sweat more due to high physical activity and their environment, two Australian and NATO reports were established with sodium levels that were twice as high as that of the civilian public (460-2,300 mg/day to 920-3,200 mg/day). Increasing sodium intake to <4,800 mg/day is recommended for individuals who sweat a lot or are not accustomed to hot environments. The DRI in Australia, the UK, USA, and NATO consider the physical activity and operating environment of military personnel, differing from those of the civilian population in terms of (1) energy requirements, (2) protein, (3) macronutrient energy distribution, and (4) sodium.

Background: Some studies on weight loss promotion using smartphone apps have shown a weight loss effect but not an increase in physical activity. However, the long-term effects of smartphone apps on weight loss and increasing physical activity have not been rigorously examined to date. Objective: The aim of this study was to assess whether the use of a smartphone app will increase physical activity and reduce body weight. Methods: In this parallel randomized clinical trial, participants recruited between April 2018 and June 2019 were randomized in equal proportions to a smartphone app group (n=55) or a control group (n=54). The intention-to-treat approach was used to analyze the data from December 2019 through November 2021. Before the intervention, an hour-long lecture on weight loss instruction and increasing physical activity was conducted once for both groups. Participants in both groups were instructed to weigh themselves immediately after waking up at least once daily from the start of the intervention. Monthly emails were sent advising the participants in both groups on how to lose weight and increase physical activity in order to maintain or increase motivation. Participants in the smartphone app group were instructed to open the app at least once a day to check their step count and rank. The primary outcome was daily accelerometer-measured physical activity (step count) and the secondary outcome was body weight. Since there was a significant difference in the wear time of the accelerometer depending on the intervention period (P<.001), the number of steps and moderate-to-vigorous physical activity were also evaluated per wear time. Results: The mean age of the 109 participants in this study was 47 (SD 8) years. At baseline, the mean daily total steps were 7259 (SD 3256) steps per day for the smartphone app group and 8243 (SD 2815) steps per day for the control group. The difference in the step count per wear time between preintervention and postintervention was significantly different between the app group and the control group (average difference [95% CI], 65 [30 to 101] steps per hour vs –9 [–56 to 39] steps per hour; P=.042). The weight loss was –2.2 kg (SD –3.1%) in the smartphone app group and –2.2 kg (SD –3.1%) in the control group, with no significant difference between the groups. In addition, when divided into weekdays (Monday through Friday) and weekends (Saturday and Sunday), there was a significant interaction between step counts (P=.004) and MVPA (P=.003) during the intervention, with the app group showing higher interaction on weekends than the control group. Conclusions: In this trial, the group with the smartphone app intervention showed increased physical activity, especially on weekends. However, this increased physical activity did not lead to increased weight loss. Trial Registration: University Hospital Medical Information Network UMIN000033397; https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000037956