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Many activity trackers have been developed, but steps can still be inconsistent from one monitor to another.What are the differences and associations between the steps of 13 selected consumer-based and research-grade wearable devices during 1 standardized day in a metabolic chamber and 15-day free-living trials?In total, 19 healthy adults between 21 and 50 years-old participated in this study. Participants were equipped with 12 accelerometer-based active trackers and one pedometer (Yamasa) in order to monitor the number of steps per day. The devices were worn on the waist (ActiGraph, Omron, Actimarker, Lifedorder, Withings, and Yamasa) or non-dominant wrist (Fitbit, Garmin, Misfit, EPSON, and Jawbone), or placed in a pocket (Omron CaloriScan, and TANITA). Participants performed structured activities over a 24 h period in a chamber (Standardized day), and steps were monitored in the same participants in free-living trials for 15 successive days using the same monitors (free-living days).When the 13 monitors were ranked by their steps, waist-worn ActiGraph was located at the center (7th) of the monitors both in the Standardized (12,252 ± 598 steps/day, mean ± SD) and free-living days (9295 ± 4027 steps/day). The correlation between the accelerometer-based devices was very high (r = 0.87-0.99). However, the steps of Yamasa was significantly lower in both trials than ActiGraph. The wrist-worn accelerometers had significantly higher steps than other devices both trials (P 0.05). The differences between ActiGraph and Actimarker or Lifecorder was less than 100 steps/day in the Standardized day, and the differences between ActiGraph and Active Style Pro was less than 100 steps/day in the free-living days. Regression equation was also performed for inter-device compatibility.Step obtained from the wrist-worn, waist-worn, and pocket-type activity trackers were significantly different from each other but still highly correlated in free-living conditions.

The Dietary Reference Intakes 2020 divided the older population into those aged 65-74 y and those over 75 y old. However, physical activity level in each age group was not specified. This study examined age-related differences in physical activity level among healthy Japanese older people, and the effect of lifestyles on these differences. In total, 70 people (22 men, 48 women) aged 65-85 y old participated in this study. Total energy expenditure was measured using the doubly labeled water method, and basal metabolic rate using expired gas concentration and volume. The Physical Activity Scale for the Elderly and a triaxial accelerometer were used to assess physical activities. Physical activity level was significantly higher among 65-74 y old (median 1.86) than those over 75 y old (1.76). However, the Physical Activity Scale for the Elderly did not show any significant differences between the age groups. The duration of physical activity with 3.0-5.9 metabolic equivalents was longer for both locomotive and household activities among 65-74 y old than those over 75 y old. Younger participants walked a median of 6,364 steps a day, compared with 4,419 steps for older people. The 65-74 y old participants involved in paid work or who habitually exercised, and those over 75 y old taking more than 40 min a day of moderate to vigorous physical activity, and walking more than the median level for their sex and age group had significantly higher physical activity levels.

Malnutrition is a severe problem among elderly residents living in long-term care facilities. A simple and accurate estimation for total daily energy expenditure (TEE) is required in order to provide them with an adequate amount of food. The purpose of this study was to validate a physical activity assessment tool for estimating TEE among elderly residents. The data of 58 subjects aged 69-99 y were analyzed in this study. The one-day physical activity recall was filled out by facility staff for a typical day. TEE was measured by the doubly labeled water (DLW) method (TEEDLW), and predicted by one-day physical activity recall using the basal metabolic rate (pTEEBMR) and body weight (pTEEBW). The TEEDLW, pTEEBMR and pTEEBW were 1,129±196 kcal/d, 1,186±251 kcal/d and 1,326±236 kcal/d, respectively. In a regression model, body weight, movement means and sex explained 39.0% of the variance in the difference between pTEEBMR and TEEDLW (p

Unintentional weight loss is a major frailty component; thus, assessing energy imbalance is essential for institutionalized elderly residents. This study examined prediction errors of the observed energy intake (OEI) against the actual energy intake obtained from the doubly labeled water (DLW) method and clarified the relationship between the energy gap obtained by subtracting total energy expenditure (TEE) from OEI and subsequent weight changes in elderly residents in long-term care facilities. Overall, 46 participants were recruited in Japan. TEE was measured using the DLW method, and OEI was calculated from recipes and plate waste simultaneously over a 14–15-day period at baseline. The total energy intake (TEIDLW) was determined on the basis of DLW and weight changes during the DLW period. The weight was longitudinally tracked monthly for 12 months in the 28 residents who still lived at the facilities. OEI was higher than TEIDLW by a mean of 232 kcal/day (15.3%) among 46 residents at baseline. The longitudinal data of 28 residents showed that the energy gap tended to be correlated with the slope of weight change (ρ = 0.337, p = 0.080) and the median value was significantly lower in the weight loss group (152 kcal/day) than in the weight gain group (350 kcal/day) (p < 0.05). In conclusion, weight loss could occur at Japanese long-term care facilities even if the difference obtained by subtracting TEE from OEI was positive because OEI was overestimated by more than 200 kcal/day.

Background & aims:Although accurate assessment of energy intake (EI) is critical in diabetes care, underreporting of EI on dietary records (DR) is often an issue. However, few studies have examined EI with doubly labeled water (DLW) in patients with diabetes mellitus. We aimed to investigate the impact of sex and obesity on the dissociation of DR from total energy expenditure (TEE) evaluated with DLW in patients with type 2 diabetes., Methods:Fifty-two patients with type 2 diabetes aged 60–79 years were enrolled for the Clinical Evaluation of Energy Requirements in Patients with Diabetes Mellitus (CLEVER-DM) study at a single university hospital. TEE was measured over 14 days by the DLW method as standard. EI was calculated by assessment of 3-day DR by registered dietitians., Results:The mean difference between EI and TEE was 238 ± 412 kcal/day (~10% of TEE). Neither EI nor TEE was significantly different between obese (body mass index (BMI) ≥25 kg/m 2) and non-obese (BMI, Conclusions:EI calculated by 3-day DR may underestimate habitual intake, which is assumed to be equal to TEE measured by the DLW method except in non-obese women with diabetes., Clinical trial registration number:UMIN000023051.

Background Physical inactivity due to cachexia and muscle wasting is well recognized as a sign of poor prognosis in chronic obstructive pulmonary disease (COPD). However, there have been no reports on the relationship between trunk muscle measurements and energy expenditure parameters, such as the total energy expenditure (TEE) and physical activity level (PAL), in COPD. In this study, we investigated the associations of computed tomography (CT)-derived muscle area and density measurements with clinical parameters, including TEE and PAL, in patients with or at risk for COPD, and examined whether these muscle measurements serve as an indicator of TEE and PAL. Methods The study population consisted of 36 male patients with (n = 28, stage 1–4) and at risk for (n = 8) COPD aged over 50 years. TEE was measured by the doubly labeled water method, and PAL was calculated as the TEE/basal metabolic rate estimated by the indirect method. The cross-sectional areas and densities of the pectoralis muscles, rectus abdominis muscles, and erector spinae muscles were measured. We evaluated the relationship between these muscle measurements and clinical outcomes, including body composition, lung function, muscle strength, TEE, and PAL. Results All the muscle areas were significantly associated with TEE, severity of emphysema, and body composition indices such as body mass index, fat-free mass, and trunk muscle mass. All trunk muscle densities were correlated with PAL. The product of the rectus abdominis muscle area and density showed the highest association with TEE (r = 0.732) and PAL (r = 0.578). Several trunk muscle measurements showed significant correlations with maximal inspiratory and expiratory pressures, indicating their roles in respiration. Conclusions CT-derived measurements for trunk muscles are helpful in evaluating physical status and function in patients with or at risk for COPD. Particularly, trunk muscle evaluation may be a useful marker reflecting TEE and PAL.

Although weight loss suggests poor prognosis of COPD, only a few studies have examined total energy expenditure (TEE) or physical activity level (PAL) using the doubly labelled water (DLW) method. We evaluated TEE and PAL using the DLW method together with a triaxial accelerometer to elucidate the relationships between TEE, PAL and clinical parameters leading to a practical means of monitoring COPD physical status. This study evaluated 50- to 79-year-old male patients with mild to very severe COPD (n=28) or at risk for COPD (n=8). TEE, activity energy expenditure for 2 weeks and basal metabolic rate were measured by DLW, an accelerometer and indirect calorimetry, respectively. All patients underwent pulmonary function, chest-computed tomography, 6-min walk test, body composition and grip strength tests. Relationships between indices of energy expenditure and clinical parameters were analysed. Bland–Altman analysis was used to examine the agreement of TEE and PAL between the DLW method and the accelerometer. TEE and PAL using DLW in the total population were 2273±445 kcal·day−1 and 1.80±0.20, respectively. TEE by DLW correlated well with that from the accelerometer and grip strength (p, Although total energy expenditure and physical activity level in COPD patients were underestimated by an accelerometer, they can be predicted using indices of the accelerometer and clinical parameters such as the 6-min walk distance and grip strength. https://bit.ly/2OYbuaI

Background: Heart rate (HR) during physical activity is strongly affected by the level of physical fitness. Therefore, to assess the effects of fitness, we developed predictive equations to estimate the metabolic equivalent (MET) of daily activities, which includes low intensity activities, by % HR reserve (%HRR), resting HR, and multiple physical characteristics. Methods: Forty volunteers between the ages of 21 and 55 performed 20 types of daily activities while recording HR and sampling expired gas to evaluate METs values. Multiple regression analysis was performed to develop prediction models of METs with seven potential predictors, such as %HRR, resting HR, and sex. The contributing parameters were selected based on the brute force method. Additionally, leave-one-out method was performed to validate the prediction models. Results: %HRR, resting HR, sex, and height were selected as the independent variables. %HRR showed the highest contribution in the model, while the other variables exhibited small variances. METs were estimated within a 17.3% difference for each activity, with large differences in document arrangement while sitting (+17%), ascending stairs (&minus, 8%), and descending stairs (+8%). Conclusions: The results showed that %HRR is a strong predictor for estimating the METs of daily activities. Resting HR and other variables were mild contributors. (201 words)

Background Meals, particularly carbohydrate intake, determine diurnal blood glucose (BG) excursions. However, the effect of meals with variable carbohydrate content on diurnal BG excursions remains poorly understood, despite routine consumption of meals that vary daily. Objective The aim of this study was to verify our hypothesis that glycemic response is elevated when a meal with a higher carbohydrate content follows a meal with a lower carbohydrate content. Design This was a secondary analysis of a study whose primary endpoint was energy metabolism (e.g., energy expenditure and substrate oxidation). This crossover study was designed to test BG responses to 3 types of meals with different macronutrient contents [regular meals (R), meals with a high-carbohydrate breakfast (CB), and meals with a high-fat breakfast (FB)] using a continuous glucose monitoring system. The R test included 3 meals/d with the same macronutrient composition; the CB test, a high-carbohydrate meal at breakfast, a high-fat meal at lunch, and a high-carbohydrate meal at dinner; and the FB test, a high-fat meal at breakfast, a high-carbohydrate meal at lunch, and a high-carbohydrate meal at dinner. Each test had similar daily macronutrient compositions, except CB and FB had larger variations in carbohydrate content than R. Fourteen healthy young men were tested in random order and underwent whole-body indirect calorimetry. Results Daily peak BG concentrations were higher for the CB (mean ± SD: 143.9 ± 25.3 mg/dL) and FB (140.2 ± 24.8 mg/dL) conditions than for the R condition (127.5 ± 15.7 mg/dL). Postprandial BG peaks after a high-carbohydrate meal were ∼20 mg/dL higher when a previous meal was relatively high-fat than when not high-fat (P < 0.05 for all). A multiple regression analysis indicated that the postprandial glycemic response was negatively associated with the preprandial respiratory quotient. Conclusions Our findings indicate that switching from high-fat to high-carbohydrate meals contributes to larger postprandial BG excursions, along with alterations in prioritization of carbohydrate utilization. This study was registered at the UMIN Clinical Trials Registry as UMIN000028895.

Background and objectiveWeight loss and reduced fat-free mass are independent risk factors for mortality among patients with chronic obstructive pulmonary disease (COPD). These factors are important for determining diet therapy and examining the validity of assessment for energy intake (EI). We assessed the agreement of EI between a brief-type self-administered diet history questionnaire (BDHQ) and the doubly labelled water (DLW) method among male patients with stable/at risk for COPD.MethodIn this cross-sectional observational study, data for 33 male patients were analysed. At the first visit, EI was estimated using a BDHQ (EIBDHQ). Total energy expenditure (TEE) was measured during 13–15 days by the DLW method, while corrected EI was calculated using the TEE and weight change during the DLW period (EIDLW). The difference between EIBDHQ and EIDLW was evaluated by the Bland-Altman method. Multiple regression analysis was used to determine the proportion of variance in the difference between EIBDHQ and EIDLW, as determined by the patient’s characteristics.ResultsEIBDHQ was 2100 (95% CI: 1905 to 2295) kcal/day in the total population. A fixed bias was observed between EIBDHQ and EIDLW as −186 (95% CI: −422 to 50) kcal/day, while a proportional bias was not detected by the Bland-Altman analysis. Age, weight, anxiety and interleukin 6 were responsible for 61.7% of the variance in the difference between both EIs in a multiple regression model.ConclusionsThe BDHQ underestimated EI among male patients with stable/at risk for COPD, but this estimation error was within an acceptable range compared with previous studies. EIBDHQ precision might be improved by considering common COPD traits, including inflammatory condition and mental state.

OBJECTIVES: In this study we investigated the effects of hydrogen (H(2)) supplement, in the form of calcium bound H(2) powder capsules, on aerobic and anaerobic cycling exercise. METHODS: Trained male subjects participated in a randomized, double-blind, crossover design trial and received H(2)-rich calcium powder (HCP) supplement (1500 mg/day, 2.544 μg/day of H(2) gas) or H(2)-depleted placebo (PLA) (1500 mg/day) for 3 consecutive days. Aerobic experiment: Eighteen subjects carried out a cycling incremental exercise starting at 20 watts (W) work rate, and increasing by 20 W/2 minutes until exhaustion. Blood gases including pH, bicarbonate ion (HCO(3)(−)), partial pressures of CO(2) (PCO(2)), metabolic profile including lactate (Lac), and electrolytes including chloride (Cl(−)) were measured at rest and at 120-, 200-, and 240-W work rates. Anaerobic experiment: Six subjects carried out a 30 second Wingate anaerobic exercise test (WAnT) bout with a resistive load of 7.5% of body mass. Lac was then measured at 1-, 3-, 5-, and 15-minutes following the WAnT exercise. RESULTS: Aerobic experiment: At rest, HCP had a significantly lower pH (P = 0.048), Cl(−) (P = 0.011), and a higher HCO(3)(−) (P = 0.041), PCO(2) (P = 0.026) compared to the PLA group. During exercise, pH decrease (supplement effect: P = 0.043) and the HCO(3)(−) increase (supplement effect: P = 0.030) continued in the HCP group. Additionally, HCP did not affect peak work load and exercise duration. And no changes were noted in Lac at rest or during exercise. Anaerobic experiment: HCP did not affect peak power output or Lac recovery following WAnT. However the average power output during exercise was significantly higher in the HCP group (P = 0.019) compared to the PLA group. CONCLUSIONS: HCP supplementation following 3 days of intake, slightly lowered pH during aerobic exercise, and increased average power output in the anaerobic WAnT exercise compared to the PLA group. HCP supplement might have an ergogenic effect in an anaerobic exercise setting. FUNDING SOURCES: This study was supported by The Japan Society for the Promotion of Science, and the Ministry of Education, Science, and Culture of Japan. In addition, we received a research grant from the company ENAGEGATE Inc. and they provided the HCP supplement and placebo. However, no intercession, restrictions or agreements of any kind was imposed between parties regarding research design, results or publications.

Background To establish Japanese children’s estimated energy requirements, total energy expenditure (TEE) data measured using the doubly labeled water (DLW) method is needed. This study aimed to 1) obtain basic TEE data from Japanese children measured using DLW (TEEDLW), 2) compare TEEDLW with TEE estimated by various estimation formulas to calculate their accuracy, and 3) develop a new equation to estimate TEE using body composition and pedometers. Methods TEE was measured using DLW in 56 10- to 12-year-old Japanese children (33 boys, 23 girls). Physical activity level (PAL) was calculated by dividing TEEDLW by estimated resting energy expenditure. To assess their physical activity, participants wore pedometers during the 7-d DLW period. Total body water was calculated from 2H and 18O; fat-free mass (FFM) and fat mass (FM) were then determined. Results In boys and girls of normal weight, TEEDLW was 2067 ± 230 kcal/d and 1830 ± 262 kcal/d, respectively. Average PAL was 1.58 ± 0.17. FFM was strongly related to TEE (r = 0.702, p

BackgroundA triaxial accelerometer with an algorithm that could discriminate locomotive and non-locomotive activities in adults has been developed. However, in the elderly, this accelerometer has not yet been validated. The aim were to examine the validity of this accelerometer in the healthy elderly, and to compare the results with those derived in a healthy younger sample.MethodsTwenty-nine healthy elderly subjects aged 60–80 years (Elderly), and 42 adults aged 20–59 years (Younger) participated. All subjects performed 11 activities, including locomotive and non-locomotive activities with a Douglas bag while wearing the accelerometer (Active style Pro HJA-750C). Physical activity intensities were expressed as metabolic equivalents (METs). The relationship between the METs measured using the Douglas bag and METs predicted using the accelerometer was evaluated.ResultsA significant correlation between actual and predicted METs was observed in both Elderly (r=0.85, pConclusionThe degree of correlation between predicted and actual METs was comparable in elderly and younger participants, but the prediction errors were greater in elderly participants, particular at higher-intensity activities, which suggests that different predicting equations may be needed for the elderly.

Introduction: Evaluation of total energy expenditure (TEE) and physical activity level (PAL) is important for treatment of patients with type 2 diabetes mellitus (T2DM). However, the validity of accelerometers (ACC) and physical activity questionnaires (PAQ) for estimating TEE and PAL remains unknown in elderly populations with T2DM. We evaluated the accuracy of TEE and PAL results estimated by an ACC (TEEACC and PALACC) and a PAQ (TEEPAQ and PALPAQ) in elderly patients with T2DM. Methods: Fifty-one elderly patients with T2DM (aged 61–79 years) participated in this study. TEEACC was calculated with PALACC using a triaxial ACC (Active style Pro HJA-750c) over 2 weeks and predicted basal metabolic rate (BMR) by Ganpule’s equation. TEEPAQ was estimated using predicted BMR and the PALPAQ from the ­Japan Public Health Center Study-Long questionnaire. We compared the results to TEEDLW measured with the doubly labeled water (DLW) method and PALDLW calculated with BMR using indirect calorimetry. Results: TEEDLW was 2,165 ± 365 kcal/day, and TEEACC was 2,014 ± 339 kcal/day; TEEACC was strongly correlated with TEEDLW (r = 0.87, p < 0.01) but significantly underestimated (–150 ± 183 kcal/day, p < 0.05). There was no significant difference in TEEPAQ and TEEDLW (–49 ± 284 kcal/day), while the range of difference seemed to be larger than TEEACC. PALDLW, PALACC, and PALPAQ were calculated to be 1.71 ± 0.17, 1.69 ± 0.16, and 1.78 ± 0.24, respectively. ­PALACC was strongly correlated with PALDLW (r = 0.71, p < 0.01), and there was no significant difference between the 2 values. PALPAQ was moderately correlated with PALDLW (r = 0.43, p < 0.01) but significantly overestimated. Predicted BMR was significantly lower than the BMR ­measured by indirect calorimetry (1,193 ± 186 vs. 1,262 ± 155 kcal/day, p < 0.01). Conclusions: The present ACC and questionnaire showed acceptable correlation of TEE and PAL compared with DLW method in elderly patients with T2DM. Systematic errors in estimating TEE may be improved by the better equation for predicting BMR.

Graphical abstract, Diet-induced thermogenesis (DIT) refers to energy expenditure (EE) related to food consumption. Enhancing DIT can lead to weight loss. Factors that increase DIT are expected to lower body mass index and body fat mass. Although various methods have been developed for measuring DIT in humans, there is currently no method available for calculating absolute DIT values in mice. Therefore, we attempted to measure DIT in mice by applying the method more commonly used for humans. Mouse energy metabolism was first measured under fasting conditions; EE was plotted against the square root of the activity count, and a linear regression equation was fit to the data. Then, energy metabolism was measured in mice that were allowed to feed ad libitum, and EE was plotted in the same way. We calculated the DIT by subtracting the predicted EE value from the fed EE value for the same activity count. The methodology for measuring DIT in mice may be helpful for researching ways of combatting obesity by increasing DIT. • The methodology for measuring absolute DIT values in mice is developed. • For mice, the proportion of DIT compared with calorie intake and EE are 12.3% and 21.7%, respectively.

BACKGROUND Self-monitoring using certain types of pedometers and accelerometers has been reported to be effective for promoting and maintaining physical activity (PA). However, the validity of estimating the level of PA or PA energy expenditure (PAEE) for general consumers using wearable devices has not been sufficiently established. OBJECTIVE We examined the validity of 12 wearable devices for determining PAEE during 1 standardized day in a metabolic chamber and 15 free-living days using the doubly labeled water (DLW) method. METHODS A total of 19 healthy adults aged 21 to 50 years (9 men and 10 women) participated in this study. They followed a standardized PA protocol in a metabolic chamber for an entire day while simultaneously wearing 12 wearable devices: 5 devices on the waist, 5 on the wrist, and 2 placed in the pocket. In addition, they spent their daily lives wearing 12 wearable devices under free-living conditions while being subjected to the DLW method for 15 days. The PAEE criterion was calculated by subtracting the basal metabolic rate measured by the metabolic chamber and 0.1×total energy expenditure (TEE) from TEE. The TEE was obtained by the metabolic chamber and DLW methods. The PAEE values of wearable devices were also extracted or calculated from each mobile phone app or website. The Dunnett test and Pearson and Spearman correlation coefficients were used to examine the variables estimated by wearable devices. RESULTS On the standardized day, the PAEE estimated using the metabolic chamber (PAEEcha) was 528.8±149.4 kcal/day. The PAEEs of all devices except the TANITA AM-160 (513.8±135.0 kcal/day; P>.05), SUZUKEN Lifecorder EX (519.3±89.3 kcal/day; P>.05), and Panasonic Actimarker (545.9±141.7 kcal/day; P>.05) were significantly different from the PAEEcha. None of the devices was correlated with PAEEcha according to both Pearson (r=−.13 to .37) and Spearman (ρ=−.25 to .46) correlation tests. During the 15 free-living days, the PAEE estimated by DLW (PAEEdlw) was 728.0±162.7 kcal/day. PAEE values of all devices except the Omron Active style Pro (716.2±159.0 kcal/day; P>.05) and Omron CaloriScan (707.5±172.7 kcal/day; P>.05) were significantly underestimated. Only 2 devices, the Omron Active style Pro (r=.46; P=.045) and Panasonic Actimarker (r=.48; P=.04), had significant positive correlations with PAEEdlw according to Pearson tests. In addition, 3 devices, the TANITA AM-160 (ρ=.50; P=.03), Omron CaloriScan (ρ=.48; P=.04), and Omron Active style Pro (ρ=.48; P=.04), could be ranked in PAEEdlw. CONCLUSIONS Most wearable devices do not provide comparable PAEE estimates when using gold standard methods during 1 standardized day or 15 free-living days. Continuous development and evaluations of these wearable devices are needed for better estimations of PAEE.

To maintain workplace safety and to achieve an effective wellness management for physical workers, we propose applications of smart clothing and a cyber-physical system-based approach. As a potential application of our approach, we here introduce a heat stress assessment system for physical workers. In our system, worker's bio-information, such as heart rate, physical activity and temperature inside clothing, is measured using smart clothing. In addition, environmental parameters, such as air temperature and relative humidity and weather forecast are given through the connection to a weather company's system. All data sets are integrated to a cloud platform and analyzed in real-time manner. Using this system, we can achieve an effective heat stress assessment and management. In our talk, we also discuss further applications of the cyber-physical system-based approach.

We investigated effects of molecular hydrogen (H2) supplementation on acid-base status, pulmonary gas exchange responses, and local muscle oxygenation during incremental exercise. Eighteen healthy, trained subjects in a randomized, double-blind, crossover design received H2-rich calcium powder (HCP) (1500 mg/day, containing 2.544 µg/day of H2) or H2-depleted placebo (1500 mg/day) for three consecutive days. They performed cycling incremental exercise starting at 20-watt work rate, increasing by 20 watts/2 min until exhaustion. Breath-by-breath pulmonary ventilation (VE) and CO2 output (VCO2) were measured and muscle deoxygenation (deoxy[Hb + Mb]) was determined via time-resolved near-infrared spectroscopy in the vastus lateralis (VL) and rectus femoris (RF). Blood gases’ pH, lactate, and bicarbonate (HCO3−) concentrations were measured at rest and 120-, 200-, and 240-watt work rates. At rest, the HCP group had significantly lower VE, VCO2, and higher HCO3−, partial pressures of CO2 (PCO2) versus placebo. During exercise, a significant pH decrease and greater HCO3− continued until 240-watt workload in HCP. The VE was significantly lower in HCP versus placebo, but HCP did not affect the gas exchange status of VCO2 or oxygen uptake (VO2). HCP increased absolute values of deoxy[Hb + Mb] at the RF but not VL. Thus, HCP-induced hypoventilation would lead to lower pH and secondarily impaired balance between O2 delivery and utilization in the local RF during exercise, suggesting that HCP supplementation, which increases the at-rest antioxidant potential, affects the lower ventilation and pH status during incremental exercise. HPC induced a significantly lower O2 delivery/utilization ratio in the RF but not the VL, which may be because these regions possess inherently different vascular/metabolic control properties, perhaps related to fiber-type composition.

It is effective to remove false dependencies to efficiently perform out-of-order (OoO) execution which improves single thread performance. Hardware register renaming removes these dependencies, but it is one of the bottlenecks of the processor because of its complexity. The use of a STRAIGHT architecture is one of the approaches that allow the compiler to remove these dependencies. Because the source operand is specified as the distance between the producer instruction and consumer instruction and there is no register overwriting, no false dependency occurs. Instead, the compiler must generate code that satisfies the constraint of specifying operands as constant distances that are not dependent on the execution path. Although the basic algorithms for realizing the constraint are already known, the machine code generated thereby cannot achieve high performance because it is necessary to execute many inter-register transfer instructions added by compiler to satisfy the constraints. This paper presents an efficient algorithm that uses data flow analysis to determine the value causing an increase in the number of executed instructions and improve the performance by spilling them on the stack. We developed a compiler that implements the proposed method using LLVM and evaluated using CoreMark as a benchmark. The number of executed instructions was reduced by approximately 31 %, and the execution performance improved by up to 32 %.

The single-thread performance of a processor improves the capability of the entire system by reducing the critical path latency of programs. Typically, conventional superscalar processors improve this performance by introducing out-of-order (OoO) execution with register renaming. However, it is also known to increase the complexity and affect the power efficiency. This paper realizes a novel computer architecture called "STRAIGHT" to resolve this dilemma. The key feature is a unique instruction format in which the source operand is given based on the distance from the producer instruction. By leveraging this format, register renaming is completely removed from the pipeline. This paper presents the practical Instruction Set Architecture (ISA) design, the novel efficient OoO microarchitecture, and the compilation algorithm for the STRAIGHT machine code. Because the ISA has sequential execution semantics, as in general CPUs, and is provided with a compiler, programming for the architecture is as easy as that of conventional CPUs. A compiler, an assembler, a linker, and a cycle-accurate simulator are developed to measure the performance. Moreover, an RTL description of STRAIGHT is developed to estimate the power reduction. The evaluation using standard benchmarks shows that the performance of STRAIGHT is 18.8% better than the conventional superscalar processor of the same issue-width and instruction window size. This improvement is achieved by STRAIGHT's rapid miss-recovery. Compilation technology for resolving the possible overhead of the ISA is also revealed. The RTL power analysis shows that the architecture reduces the power consumption by removing the power for renaming. The revealed performance and efficiencies support that STRAIGHT is a novel viable alternative for designing general purpose OoO processors.

Background Physical activity questionnaires (PAQs) used in large-scale Japanese cohorts have rarely been simultaneously validated against the gold standard doubly labeled water (DLW) method. This study examined the validity of seven PAQs used in Japan for estimating energy expenditure against the DLW method. Methods Twenty healthy Japanese adults (9 men; mean age, 32.4 [standard deviation {SD}, 9.4] years, mainly researchers and students) participated in this study. Fifteen-day daily total energy expenditure (TEE) and basal metabolic rate (BMR) were measured using the DLW method and a metabolic chamber, respectively. Activity energy expenditure (AEE) was calculated as TEE - BMR - 0.1 × TEE. Seven PAQs were self-administered to estimate TEE and AEE. Results The mean measured values of TEE and AEE were 2,294 (SD, 318) kcal/day and 721 (SD, 161) kcal/day, respectively. All of the PAQs indicated moderate-to-strong correlations with the DLW method in TEE (rho = 0.57-0.84). Two PAQs (Japan Public Health Center Study [JPHC]-PAQ Short and JPHC-PAQ Long) showed significant equivalence in TEE and moderate intra-class correlation coefficients (ICC). None of the PAQs showed significantly equivalent AEE estimates, with differences ranging from -547 to 77 kcal/day. Correlations and ICCs in AEE were mostly weak or fair (rho = 0.02-0.54, and ICC = 0.00-0.44). Only JPHC-PAQ Short provided significant and fair agreement with the DLW method. Conclusions TEE estimated by the PAQs showed moderate or strong correlations with the results of DLW. Two PAQs showed equivalent TEE and moderate agreement. None of the PAQs showed equivalent AEE estimation to the gold standard, with weak-to-fair correlations and agreements. Further studies with larger sample sizes are needed to confirm these findings.