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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

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.

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.

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.

We compared the physical activity level (PAL) and training and non-training physical activity (PA) between rhythmic gymnasts and lacrosse players. In addition, we aimed to clarify the contribution of training and non-training PA to the PAL. Our study subjects were 11 female rhythmic gymnasts and 11 female lacrosse players. PAL was calculated from the total energy expenditure (TEE) as assessed by the doubly labeled water (DLW) method, and the resting metabolic rate (RMR) was measured using indirect calorimetry. Daily PA and sleep durations were assessed using an activity diary. The intensity (metabolic equivalent, MET) of non-training PA was measured using a tri-axial accelerometer. The amount (MET･h) of training was calculated by subtracting the amount of PA and sleep outside of training from the TEE. There were no significant differences in PAL between rhythmic gymnasts (2.59 ± 0.63) and lacrosse players (2.43 ± 0.46). Rhythmic gymnasts had a longer duration and larger amount of training PA and a shorter duration and smaller amount of non-training PA than did lacrosse players. The mean intensities of training and non-training PA were not significantly different between the groups. PAL was positively correlated with the amount of training in both rhythmic gymnasts (γs = 0.818) and lacrosse players (γs = 0.882). There were no significant relationships between PAL and non-training PA in both groups. Our results indicate that the amount of training strongly affects PAL in these athletes.

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%.

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.

Background The purpose of this study was to examine the change in accelerometer-derived daily physical activity (PA) and activity record-derived daily activities over a 10-year period in urban elementary schoolchildren in Japan. Methods A total of 233 sixth-grade children (11–12 years old) in a same elementary school in Kyoto participated in the study (n = 125 and 108 in 1999 and 2009, respectively). The participant rate is 91.9 and 98.2 % in 1999 and 2009, respectively. The size and shape of the school district was not changed. The children were instructed to wear an accelerometer for 5 consecutive days of weekday and to keep minute-by-minute 24-h activity records with the assistance of their parents. The school-day scheduling was similar between 1999 and 2009 (29 and 27 sessions of 45-min class per week, respectively). Results From 1999 to 2009, step counts considerably decreased (20,832 vs. 12,237 steps per day in boys and 16,087 vs. 10,748 steps per day in girls; P

Pedometers are used in many studies because physical activities can be simply assessed using them. In addition to the spring-levered type, piezo-electric pedometers have recently been used. However, their accuracy in children has not been investigated in previous studies. In this study, we investigated the accuracy of spring-levered pedometer and piezo-electric pedometer step counts in comparison with hand-tallied step counts with children during self-paced walking. First to 6th-grade primary school children (394 subjects; 201 boys and 193 girls) walked with pedometers on an outdoor 50-m course, and the measured values were compared with actual steps at 3 speeds: normal, slow, and fast paces. The counts obtained by the spring-levered pedometer were significantly lower than the actual steps in most grades at all walking paces. In the normal- and fast-pace walking, the counts by spring-levered pedometer were significantly lower than the actual steps in all grades excluding the 5th grade. Moreover, the spring-levered pedometer underestimated by 25% or more in all grades at slow pace. In contrast, the percent error of the counts by the piezo-electric pedometers at normal pace were mostly within +/-3%, confirming their high accuracy. Based on these findings, spring-levered pedometers are not appropriate for children, whereas piezo-electric pedometers are useful for investigation of the physical activity of children.