Dietary macronutrient composition and its effect on 24-h substrate oxidation: A study of diurnal variations in carbohydrate and fat intake.

BACKGROUND & AIMS: In recent times, the complexity of food styles and meal content has increased, leading to significant variations in macronutrient composition between meals. This phenomenon has coincided with a rise in obesity rates. We aimed to determine whether a large variation in macronutrient composition between meals results in reduced fat oxidation. METHODS: A cross-over study was conducted with 13 healthy young men, using whole-body indirect calorimetry to test 24-h energy metabolic responses under three conditions: regular meals (R), high-carbohydrate breakfast (CB), or high-fat breakfast (FB), each with different macronutrient contents. The R condition included three meals daily with the same macronutrient composition. The CB condition included a high-carbohydrate meal at breakfast, high-fat meal at lunch, and high-carbohydrate meal at dinner. The FB condition included a high-fat meal at breakfast, high-carbohydrate meal at lunch, and high-carbohydrate meal at dinner. The daily macronutrient compositions were similar across the three conditions, except that CB and FB had larger variations in carbohydrate-fat balance between meals than R. The participants were tested in random order. During the dietary intervention, we compared 24-h whole-body metabolic parameters, including substrate oxidation (e.g., 24 h respiratory quotient [RQ]). RESULTS: No significant differences were observed in the measures of energy expenditure among the three conditions. However, after adjusting for the sleeping RQ on a preceding day, the estimated 24hRQ was lower under the FB condition (0.845) than under the R (0.854, P = 0.0077 vs. FB) and CB conditions (0.853, P = 0.016 vs. FB). No difference was observed in the magnitude of the 5-h RQ change from lunch to dinner under the CB condition and in the magnitude of change from breakfast to lunch under the FB condition. CONCLUSIONS: A large variation in the carbohydrate-fat balance between meals does not decrease daily fat oxidation. An FB may increase daily fat oxidation compared to a CB when the daily food quotient is constant, but this increase may not be owing to the upregulation of fat burning on a daily basis.

Healthy Japanese dietary pattern is associated with slower biological aging in older men: WASEDA'S health study.

Aging is the greatest risk factor for numerous diseases and mortality, and establishing geroprotective interventions targeting aging is required. Previous studies have suggested that healthy dietary patterns, such as the Mediterranean diet, are associated with delayed biological aging; however, these associations depend on nationality and sex. Therefore, this study aimed to investigate the relationship between dietary patterns identified through principal component analysis and biological aging in older men of Japan, one of the countries with the longest life expectancies. Principal component analysis identified two dietary patterns: a healthy Japanese dietary pattern and a Western-style dietary pattern. Eight epigenetic clocks, some of the most accurate aging biomarkers, were identified using DNA methylation data from whole-blood samples. Correlation analyses revealed that healthy Japanese dietary patterns were significantly negatively or positively correlated with multiple epigenetic age accelerations (AgeAccel), including AgeAccelGrim, FitAgeAccel, and age-adjusted DNAm-based telomere length (DNAmTLAdjAge). Conversely, the Western-style dietary pattern was observed not to correlate significantly with any of the examined AgeAccels or age-adjusted values. After adjusting for covariates, the healthy Japanese dietary pattern remained significantly positively correlated with DNAmTLAdjAge. Regression analysis showed that healthy Japanese dietary pattern contributed less to epigenetic age acceleration than smoking status. These findings suggest that a Western-style dietary pattern may not be associated with biological aging, whereas a healthy Japanese dietary pattern is associated with delayed biological aging in older Japanese men. Our findings provide evidence that healthy dietary patterns may have mild beneficial effects on delayed biological aging in older Japanese men.

Polygenic Risk Score, Cardiorespiratory Fitness, and Cardiometabolic Risk Factors: WASEDA'S Health Study.

PURPOSE: This study estimated an individual's genetic liability to cardiometabolic risk factors by polygenic risk score (PRS) construction and examined whether high cardiorespiratory fitness (CRF) modifies the association between PRS and cardiometabolic risk factors. METHODS: This cross-sectional study enrolled 1,296 Japanese adults aged ≥40 years. The PRS for each cardiometabolic trait (blood lipids, glucose, hypertension, and obesity) was calculated using the LDpred2 and clumping and thresholding methods. Participants were divided into low-, intermediate-, and high-PRS groups according to PRS tertiles for each trait. CRF was quantified as peak oxygen uptake (VO 2 peak) per kg body weight. Participants were divided into low-, intermediate-, and high-CRF groups according to the tertile VO 2 peak value. RESULTS: Linear regression analysis revealed a significant interaction between PRS for triglyceride (PRS TG ) and CRF groups on serum TG levels regardless of the PRS calculation method, and attenuated the association between PRS TG and TG levels in the high-CRF group. Logistic regression analysis revealed a significant sub-additive interaction between LDpred2 PRS TG and CRF on the prevalence of high TG, indicating that high CRF attenuated the genetic predisposition to high TG. Furthermore, a significant sub-additive interaction between PRS for body mass index and CRF on obesity was detected regardless of the PRS calculation method. These significant interaction effects on high TG and obesity were diminished in the sensitivity analysis using VO 2 peak per kg fat-free mass as the CRF index. Effects of PRSs for other cardiometabolic traits were not significantly attenuated in the high-CRF group regardless of PRS calculation methods. CONCLUSIONS: The findings of the present study suggest that individuals with high CRF overcome the genetic predisposition to high TG levels and obesity.

Associations between cardiorespiratory fitness and lifestyle-related factors with DNA methylation-based ageing clocks in older men: WASEDA'S Health Study.

DNA methylation-based age estimators (DNAm ageing clocks) are currently one of the most promising biomarkers for predicting biological age. However, the relationships between cardiorespiratory fitness (CRF), measured directly by expiratory gas analysis, and DNAm ageing clocks are largely unknown. We investigated the relationships between CRF and the age-adjusted value from the residuals of the regression of DNAm ageing clock to chronological age (DNAmAgeAcceleration: DNAmAgeAccel) and attempted to determine the relative contribution of CRF to DNAmAgeAccel in the presence of other lifestyle factors. DNA samples from 144 Japanese men aged 65-72 years were used to appraise first- (i.e., DNAmHorvath and DNAmHannum) and second- (i.e., DNAmPhenoAge, DNAmGrimAge, and DNAmFitAge) generation DNAm ageing clocks. Various surveys and measurements were conducted, including physical fitness, body composition, blood biochemical parameters, nutrient intake, smoking, alcohol consumption, disease status, sleep status, and chronotype. Both oxygen uptake at ventilatory threshold (VO2 /kg at VT) and peak oxygen uptake (VO2 /kg at Peak) showed a significant negative correlation with GrimAgeAccel, even after adjustments for chronological age and smoking and drinking status. Notably, VO2 /kg at VT and VO2 /kg at Peak above the reference value were also associated with delayed GrimAgeAccel. Multiple regression analysis showed that calf circumference, serum triglyceride, carbohydrate intake, and smoking status, rather than CRF, contributed more to GrimAgeAccel and FitAgeAccel. In conclusion, although the contribution of CRF to GrimAgeAccel and FitAgeAccel is relatively low compared to lifestyle-related factors such as smoking, the results suggest that the maintenance of CRF is associated with delayed biological ageing in older men.

Coffee consumption and skeletal muscle mass: WASEDA'S Health Study.

Few studies have examined the association between coffee consumption and muscle mass; their results are conflicting. Therefore, we examined the association between coffee consumption and low muscle mass prevalence. We also performed an exploratory investigation of the potential effect modification by demographic, health status-related and physical activity-related covariates. This cross-sectional study included 2085 adults aged 40-87 years. The frequency of coffee consumption was assessed using a self-administered questionnaire. Muscle mass was assessed as appendicular skeletal muscle mass/height2 using a multifrequency bioelectrical impedance analyser. We defined low muscle mass using cut-offs recommended by the Asian Working Group for Sarcopenia. Multivariable-adjusted OR for low muscle mass prevalence were estimated using a logistic regression model. The prevalence of low muscle mass was 5·4 % (n 113). Compared with the lowest coffee consumption group (< 1 cup/week), the multivariable-adjusted OR (95 % CI) of low muscle mass prevalence were 0·62 (0·30, 1·29) for 1-3 cups/week, 0·53 (0·29, 0·96) for 4-6 cups/week or 1 cup/d and 0·28 (0·15, 0·53) for ≥ 2 cups/d (P for trend < 0·001). There were no significant interactions among the various covariates after Bonferroni correction. In conclusion, coffee consumption may be inversely associated with low muscle mass prevalence.

Fat-Free Mass Index as a Surrogate Marker of Appendicular Skeletal Muscle Mass Index for Low Muscle Mass Screening in Sarcopenia.

OBJECTIVES: We aimed to examine the relationship between the fat-free mass index (FFMI; FFM/height2) and appendicular skeletal muscle mass index (ASMI; ASM/height2), measured using both bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA), and investigate the effects of age and obesity. We also evaluated the suitability of BIA-measured FFMI as a simple surrogate marker of the ASMI and calculated the optimal FFMI cutoff value for low muscle mass screening to diagnose sarcopenia. DESIGN: Cross-sectional study. SETTING AND PARTICIPANTS: This study included 1313 adults (women, 33.6%) aged 40-87 years (mean age, 55 ± 10 years) from the WASEDA'S Health Study. METHODS: Body composition was measured using multifrequency BIA and DXA. Low muscle mass was defined according to the criteria of the Asian Working Group for Sarcopenia 2019. RESULTS: BIA-measured FFMI showed strong positive correlations with both BIA- (r = 0.96) and DXA-measured (r = 0.95) ASMIs. Similarly, in the subgroup analysis according to age and obesity, the FFMI was correlated with the ASMI. The areas under the receiver operating characteristic curve for screening low muscle mass defined by DXA-measured ASMI using BIA-measured FFMI values were 0.95 (95% CI 0.93-0.97) for men and 0.91 (95% CI 0.87-0.94) for women. The optimal BIA-measured FFMI cutoff values for screening low muscle mass defined by DXA-measured ASMI were 17.5 kg/m2 (sensitivity 89%, specificity 88%) for men and 14.6 kg/m2 (sensitivity 80%, specificity 86%) for women. CONCLUSIONS AND IMPLICATIONS: The FFMI showed a strong positive correlation with BIA- and DXA-measured ASMIs, regardless of age and obesity. The FFMI could be a useful simple surrogate marker of the ASMI for low muscle mass screening in sarcopenia in community settings. The suggested FFMI cutoff values for predicting low muscle mass are <18 kg/m2 in men and <15 kg/m2 in women.

Association Between Dietary Patterns and Different Metabolic Phenotypes in Japanese Adults: WASEDA'S Health Study.

Although many studies have reported that a posteriori dietary pattern is associated with metabolic health, there is little evidence of an association between dietary patterns and different metabolic phenotypes. The present study aimed to examine the association between major dietary patterns and different metabolic phenotypes (metabolically healthy non-obese [MHNO], metabolically unhealthy non-obese [MUNO], metabolically healthy obese [MHO], and metabolically unhealthy obese [MUO]) in middle-aged and elderly Japanese adults. This cross-sectional study enrolled 2,170 Japanese adults aged ≥40 years. The four different metabolic phenotypes were determined based on the presence of obesity, abdominal obesity, hypertension, hyperglycemia, and dyslipidemia. The major dietary patterns were determined using principal component analysis based on energy-adjusted food intake. Two dietary patterns were identified: the healthy dietary pattern, which was characterized by a high intake of vegetables, fruits, potatoes, soy products, mushrooms, seaweeds, and fish; and the alcohol dietary pattern, which was characterized by a high intake of alcoholic beverages, liver, chicken, and fish. The healthy dietary pattern was associated with the MHNO and MHO phenotypes (MUNO and MUO as reference groups, respectively), and the multivariate-adjusted odds ratios (ORs) (95% confidence intervals [CIs]) in the highest quartile of healthy dietary pattern score with the lowest quartile as the reference category were 2.10 (1.40-3.15) and 1.86 (1.06-3.25), respectively. Conversely, the alcohol dietary pattern was inversely associated with the MHNO and MHO phenotypes, while the multivariate-adjusted ORs (95% CIs) in the highest quartile of the alcohol dietary pattern score with the lowest quartile as the reference category were 0.63 (0.42-0.94) and 0.45 (0.26-0.76), respectively. There were no significant interactions between sex and healthy/alcohol dietary patterns in the prevalence of the MHNO and MHO phenotypes. In conclusion, the present study's findings suggest that major dietary patterns are associated with different metabolic phenotypes in middle-aged and elderly Japanese adults. These findings provide useful evidence for maintaining metabolic health through diet regardless of obesity status.

Association between alcohol dietary pattern and prevalence of dyslipidaemia: WASEDA'S Health Study.

The association between a dietary pattern characterised by high alcohol intake and dyslipidaemia has not been fully investigated. Therefore, the present study aimed to investigate the association between alcohol dietary patterns and the prevalence of dyslipidaemia and its components. This cross-sectional study enrolled 2171 men and women aged ≥40 years who were alumni of a Japanese university. To identify dietary patterns, a principal component analysis was performed based on the energy-adjusted food intake estimated by a brief-type self-administered diet history questionnaire. Three dietary patterns were identified, the second of which was named the alcohol dietary pattern and was characterised by a high intake of alcoholic beverages, liver, chicken and fish. This alcohol dietary pattern was associated with reduced LDL-cholesterol levels. The fully adjusted OR (95 % CI) of high LDL-cholesterol for the lowest through highest quartile of alcohol dietary pattern score were 1·00 (reference), 0·83 (0·64, 1·08), 0·84 (0·64, 1·10) and 0·68 (0·49, 0·94), respectively. Subgroup analysis showed that the alcohol dietary pattern was inversely associated with the prevalence of dyslipidaemia in women, whereas it was positively associated with high TAG levels in men. In conclusion, the alcohol dietary pattern, characterised by a high intake of alcoholic beverages, liver, chicken and fish, was associated with the prevalence of dyslipidaemia and its components. This finding provides useful information for the prevention and treatment of dyslipidaemia by modifying the diet.

Development and validation of a simple anthropometric equation to predict appendicular skeletal muscle mass.

BACKGROUND & AIMS: A limited number of studies have developed simple anthropometric equations that can be implemented for predicting muscle mass in the local community. Several studies have suggested calf circumference as a simple and accurate surrogate maker for muscle mass. We aimed to develop and cross-validate a simple anthropometric equation, which incorporates calf circumference, to predict appendicular skeletal muscle mass (ASM) using dual-energy X-ray absorptiometry (DXA). Furthermore, we conducted a comparative validity assessment of our equation with bioelectrical impedance analysis (BIA) and two previously reported equations using similar variables. METHODS: ASM measurements were recorded for 1262 participants (837 men, 425 women) aged 40 years or older. Participants were randomly divided into the development or validation group. Stepwise multiple linear regression was applied to develop the DXA-measured ASM prediction equation. Parameters including age, sex, height, weight, waist circumference, and calf circumference were incorporated as predictor variables. Total error was calculated as the square root of the sum of the square of the difference between DXA-measured and predicted ASMs divided by the total number of individuals. RESULTS: The most optimal ASM prediction equation developed was: ASM (kg) = 2.955 × sex (men = 1, women = 0) + 0.255 × weight (kg) - 0.130 × waist circumference (cm) + 0.308 × calf circumference (cm) + 0.081 × height (cm) - 11.897 (adjusted R2 = 0.94, standard error of the estimate = 1.2 kg). Our equation had smaller total error and higher intraclass correlation coefficient (ICC) values than those for BIA and two previously reported equations, for both men and women (men, total error = 1.2 kg, ICC = 0.91; women, total error = 1.1 kg, ICC = 0.80). The correlation between DXA-measured ASM and predicted ASM by the present equation was not significantly different from the correlation between DXA-measured ASM and BIA-measured ASM. CONCLUSIONS: The equation developed in this study can predict ASM more accurately as compared to equations where calf circumference is used as the sole variable and previously reported equations; it holds potential as a reliable and an effective substitute for estimating ASM.

Determinants of Resting Oxidative Stress in Middle-Aged and Elderly Men and Women: WASEDA'S Health Study.

Previous studies have not investigated the determinants of resting oxidative stress, including physical fitness, as it relates to redox regulation. The present study therefore was aimed at identifying lifestyle and biological factors that determine resting oxidative stress, including objectively measured physical fitness. In 873 middle-aged and elderly men and women, age and anthropometric parameters, lifestyle-related parameters, medication and supplementation status, physical fitness, biochemical parameters, and nutritional intake status, as well as three plasma oxidative stress markers: protein carbonyl (PC), F2-isoprostane (F2-IsoP), and thiobarbituric acid reactive substances (TBARS), were surveyed and measured. The determinants of PC, F2-IsoP, and TBARS in all participants were investigated using stepwise multiple regression analysis. In PC, age (ß = -0.11, P = 0.002), leg extension power (ß = -0.12, P = 0.008), BMI (ß = 0.12, P = 0.004), and HDL-C (ß = 0.08, P = 0.040) were included in the regression model (adjusted R 2 = 0.018). In the F2-IsoP, smoking status (ß = 0.07, P = 0.060), BMI (ß = 0.07, P = 0.054), and HbA1c (ß = -0.06, P = 0.089) were included in the regression model (adjusted R 2 = 0.006). In TBARS, glucose (ß = 0.18, P < 0.001), CRF (ß = 0.16, P < 0.001), age (ß = 0.15, P < 0.001), TG (ß = 0.11, P = 0.001), antioxidant supplementation (ß = 0.10, P = 0.002), and HbA1c (ß = -0.13, P = 0.004) were included in the regression model (adjusted R 2 = 0.071). In conclusion, the present study showed that age, anthropometric index, lifestyle-related parameters, medication and supplementation status, objectively measured physical fitness, biochemical parameters, and nutritional intake status explain less than 10% of oxidative stress at rest.

Visceral fat and cardiorespiratory fitness with prevalence of pre-diabetes/diabetes mellitus among middle-aged and elderly Japanese people: WASEDA'S Health Study.

The relationships between cardiorespiratory fitness (CRF) measurements not confounded by adiposity and the prevalence of pre-diabetes mellitus (pre-DM) and diabetes mellitus (DM) are not well known. Thus, we aimed to investigate the associations of visceral fat (VF) and CRF with the prevalence of pre-DM/DM among Japanese adults. The study included 970 individuals (327 women and 643 men) who were 40-87 years old and had complete health examinations, abdominal fat area, and fitness data from WASEDA'S Health Study during 2015-2018. The VF area was measured using magnetic resonance imaging. CRF was measured using a cycle ergometer and was defined as VO2peak divided by fat free mass. The pre-DM/DM was identified based on the questionnaire and fasting blood tests. The odds ratios (ORs) and 95% confidence intervals (CIs) for prevalence of pre-DM/DM were calculated. Seventy-three participants had pre-DM and 48 participants had DM. Compared to the low VF group, the high VF group had a higher prevalence of pre-DM/DM (OR: 1.87, 95% CI: 1.18-2.96), although no significant relationship was observed between CRF and pre-DM/DM prevalence (P for trend = 0.239). The sub-group analyses also revealed no significant relationship between CRF and pre-DM/DM prevalence in the low VF group (P for trend = 0.979), although CRF values were inversely related to the prevalence of pre-DM/DM in the high VF group (P for trend = 0.024). Although CRF was not independently related to the prevalence of pre-DM/DM after adjusting for adiposity, higher VF values were related to a higher prevalence of pre-DM/DM. In addition, CRF levels were inversely associated with the prevalence of pre-DM/DM only among high VF individuals.

Cut-offs for calf circumference as a screening tool for low muscle mass: WASEDA'S Health Study.

AIM: To re-evaluate the suitability of calf circumference as a surrogate marker of low muscle mass measured by both bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA). We also examined the effects of obesity and age on low muscle mass screening using calf circumference. METHODS: In total, 1239 adults participated in this cross-sectional study. We measured the maximum calf circumference in a standing position and appendicular skeletal muscle mass (ASM) using BIA and DXA. We defined low muscle mass based on the Asian Working Group for Sarcopenia 2019 consensus. RESULTS: Calf circumference was positively correlated with BIA-measured ASM/height2 (men: r = 0.81, women: r = 0.73) and DXA-measured ASM/height2 (men: r = 0.78, women: r = 0.76). In the subgroup analyses by obesity and age, calf circumference was also positively correlated with ASM/height2 . The optimal calf circumference cut-offs for low muscle mass screening measured by BIA and DXA were 35 cm (sensitivity 91%, specificity 84%) and 36 cm (sensitivity 82%, specificity 80%) for men, and 33 cm (sensitivity 82%, specificity 84%) and 34 cm (sensitivity 85%, specificity 72%) for women, respectively. CONCLUSIONS: Calf circumference is positively correlated with BIA- and DXA-measured muscle mass regardless of obesity and age and is a simple and accurate surrogate marker of muscle mass for diagnosing sarcopenia. Geriatr Gerontol Int 2020; 20: 943-950.

Micronutrient Intake Adequacy in Men and Women with a Healthy Japanese Dietary Pattern.

This study examined the relationship between a healthy Japanese dietary pattern and micronutrient intake adequacy based on the Dietary Reference Intakes for Japanese 2015 (DRIs-J 2015) in men and women. A cross-sectional study was conducted in 1418 men and 795 women aged 40-87 years, who participated in the Waseda Alumni's Sports, Exercise, Daily Activity, Sedentariness, and Health Study. Dietary patterns were derived from principal component analysis of the consumption of 52 food and beverage items, which were assessed by a validated brief-type self-administered diet history questionnaire. Micronutrient intakes were quantified using the dietary reference intakes score (DRIs-score) for 21 micronutrients (based on DRIs-J 2015). The healthy dietary pattern score was significantly and positively correlated with the intakes of all 21 micronutrients used for constructing the DRIs-score in men and in women (each, p < 0.001). In both sexes, the healthy dietary pattern scores were strongly and positively associated with DRIs-scores (in men: ρ = 0.806, p < 0.001; in women: ρ = 0.868, p < 0.001), and the DRIs-scores reached a plateau around the highest tertile of the healthy dietary pattern score. These results indicate that a healthy Japanese dietary pattern is associated with adequate micronutrient intakes based on the DRIs-J 2015 in both men and women.

Validity of estimating physical activity intensity using a triaxial accelerometer in healthy adults and older adults.

BACKGROUND: A 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. METHODS: Twenty-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. RESULTS: A significant correlation between actual and predicted METs was observed in both Elderly (r=0.85, p<0.001) and Younger (r=0.88, p<0.001). Predicted METs significantly underestimated compared with actual METs in both groups (p<0.001). The mean of the errors was -0.6±0.6 METs in Elderly and -0.1±0.5 METs in Younger. The degree of underestimation increased with increasing METs in Elderly (p<0.001). A stepwise multiple regression analysis revealed that predicted METs, age, and weight were related to actual METs in both groups. CONCLUSION: The 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.

Prediction of Physical Activity Intensity with Accelerometry in Young Children.

Background: An algorithm for the classification of ambulatory and non-ambulatory activities using the ratio of unfiltered to filtered synthetic acceleration measured with a triaxial accelerometer and predictive models for physical activity intensity (METs) in adults and in elementary school children has been developed. The purpose of the present study was to derive predictive equations for METs with a similar algorithm in young children. Methods: Thirty-seven healthy Japanese children (four- to six-years old) participated in this study. The five non-ambulatory activities including low-intensity activities, and five ambulatory activities were selected. The raw accelerations using a triaxial accelerometer and energy expenditure by indirect calorimetry using the Douglas bag method during each activity were collected. Results: For non-ambulatory activities, especially light-intensity non-ambulatory activities, linear regression equations with a predetermined intercept (0.9) or quadratic equations were a better fit than the linear regression. The equations were different from those for adults and elementary school children. On the other hand, the ratios of unfiltered to filtered synthetic acceleration in non-ambulatory activities were different from those in ambulatory activities, as in adults and elementary school children. Conclusions: Our calibration model for young children could accurately predict intensity of physical activity including low-intensity non-ambulatory activities.

Simple Prediction of Metabolic Equivalents of Daily Activities Using Heart Rate Monitor without Calibration of Individuals.

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 (-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).

Estimating metabolic equivalents for activities in daily life using acceleration and heart rate in wearable devices.

BACKGROUND: Herein, an algorithm that can be used in wearable health monitoring devices to estimate metabolic equivalents (METs) based on physical activity intensity data, particularly for certain activities in daily life that make MET estimation difficult. RESULTS: Energy expenditure data were obtained from 42 volunteers using indirect calorimetry, triaxial accelerations and heart rates. The proposed algorithm used the percentage of heart rate reserve (%HRR) and the acceleration signal from the wearable device to divide the data into a middle-intensity group and a high-intensity group (HIG). The two groups were defined in terms of estimated METs. Evaluation results revealed that the classification accuracy for both groups was higher than 91%. To further facilitate MET estimation, five multiple-regression models using different features were evaluated via leave-one-out cross-validation. Using this approach, all models showed significant improvements in mean absolute percentage error (MAPE) of METs in the HIG, which included stair ascent, and the maximum reduction in MAPE for HIG was 24% compared to the previous model (HJA-750), which demonstrated a 70.7% improvement ratio. The most suitable model for our purpose that utilized heart rate and filtered synthetic acceleration was selected and its estimation error trend was confirmed. CONCLUSION: For HIG, the MAPE recalculated by the most suitable model was 10.5%. The improvement ratio was 71.6% as compared to the previous model (HJA-750C). This result was almost identical to that obtained from leave-one-out cross-validation. This proposed algorithm revealed an improvement in estimation accuracy for activities in daily life; in particular, the results included estimated values associated with stair ascent, which has been a difficult activity to evaluate so far.

Effect of diurnal variations in the carbohydrate and fat composition of meals on postprandial glycemic response in healthy adults: a novel insight for the second-meal phenomenon.

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.

Validity and reproducibility of a novel method for time-course evaluation of diet-induced thermogenesis in a respiratory chamber.

We developed a novel method for computing diet-induced thermogenesis (DIT) in a respiratory chamber and evaluated the validity and reproducibility of the method. We hypothesized that DIT may be calculated as the difference between postprandial energy expenditure (EE) and estimated EE (sum of basal metabolic rate and physical activity (PA)-related EE). The estimated EE was derived from the regression equation between EE from respiration and PA intensity in the fasting state. It may be possible to evaluate the time course of DIT using this novel technique. In a validity study, we examined whether DIT became zero (theoretical value) for 6 h of fasting in 11 subjects. The mean value of DIT calculated by the novel and traditional methods was 22.4 ± 13.4 and 3.4 ± 31.8 kcal/6 h, respectively. In the reproducibility study, 15 adult subjects lived in the respiratory chamber for over 24 h on two occasions. The DIT over 15 h of postprandial wake time was calculated. There were no significant differences in the mean values of DIT between the two test days. The within-subject day-to-day coefficient of variation for calculated DIT with the novel and traditional methods was approximately 35% and 25%, respectively. The novel method did not have superior reproducibility compared with that of the traditional method. However when comparing the smaller variation in the fasting state than the theoretical value (zero), the novel method may be better for evaluating interindividual differences in DIT than the traditional method and also has the ability to evaluate the time-course.

Physical activity group classification algorithm using triaxial acceleration and heart rate.

As described in this paper, a physical activity classification algorithm is proposed for energy expenditure estimation. The proposed algorithm can improve the classification accuracy using both the triaxial acceleration and heart rate. The optimal classification also contributes to improvement of the accuracy of the energy expenditures estimation. The proposed algorithm employs three indices: the heart rate reserve (%HRreserve), the filtered triaxial acceleration, and the ratio of filtered and unfiltered acceleration. The percentage HRreserve is calculated using the heart rate at rest condition and the maximum heart rate, which is calculated using Karvonen Formula. Using these three indices, a decision tree is constructed to classify physical activities into five classes: sedentary, household, moderate (excluding locomotive), locomotive, and vigorous. Evaluation results show that the average classification accuracy for 21 activities is 91%.