Prediction of Physical Activity Intensity with Accelerometry in Young Children

Energy Expenditure of Disaster Relief Operations Estimated Using a Tri-Axial Accelerometer and a Wearable Heart Rate Monitor

The management of nutrition, food, and health for disaster relief personnel is one of the crucial aspects for carrying out effective rescue activities during large-scale natural disasters, such as a big earthquake, flooding, and landslide following heavy rainfall or man-made disasters, such as widespread fire in industrial areas. Rescue workers, such as fire fighters and rescue teams who work on the disaster relief operations, have to work long, hard, and irregular hours that require energy (both intake and expenditure), with especially altered eating patterns. Reliable estimates of the energy expenditure (TEE) for such disaster relief operations have not been fully established. Here, we propose to clarify the energy expenditure for each type of large-scale disaster activity conducted by fire fighters. Thirty fire fighters (survey participants in this research) who participated in the simulation training of large-scale disaster activities wore tri-axial accelerometers and heart rate monitors during training; and, post-training, 28 fire fighters submitted complete activity record tables. An estimation formula combining tri-axial accelerometer and heart rate monitor data was used. Additionally, energy expenditure per hour (excluding resting energy expenditure: REE) (per average body weight of participants) was calculated for 10 types of large-scale disaster response activities. We propose utilization of these data as a reference value for examining the TEE of firefighting and rescue operations in future large-scale disasters.

Accelerometer-Measured Physical Activity and Sedentary Time among Children in Japan before and during COVID-19: A Cross-Sectional and Longitudinal Analysis

This study examined changes in physical activity (PA), sedentary behavior (SB), screen time, sleep, and executive function among Japanese preschoolers between COVID-19 pre-pandemic and pandemic periods, using cross-sectional and longitudinal data. Accelerometer data from 63 children aged 5-6 years were collected from three kindergartens in Tokyo, Japan, in late 2019 (pre-COVID-19). This was compared to the data of 49 children aged 5-6 years from the same kindergartens, collected in late 2020 (during COVID-19). Sixteen children in the pre-COVID-19 cohort also participated in the 2020 survey and provided data for the longitudinal analysis. The mean minutes of PA, SB, screen time, and sleep duration, as well as executive function, were compared between the pre- and during COVID-19 cohorts. After adjusting for school, sex, and accelerometer wear time, there were no significant differences in any of the measured outcomes between the two cohorts. However, the analysis of longitudinal data revealed significant increases in time spent in SB and on screens, and a decrease in light-intensity PA and sleep duration during the pandemic compared to the pre-pandemic period. Results suggest that, despite the COVID-19 pandemic, young children's activity levels and SB did not significantly differ from pre-pandemic levels. However, school-aged children's SB, light PA, and sleep time were affected, although this cannot be disentangled from the effects of the transition to school.

Association between Water and Energy Requirements with Physical Activity and Fat-Free Mass in Preschool Children in Japan

Water and energy are essential for the human body. The doubly labeled water (DLW) method measures water turnover (WT) and total energy expenditure (TEE), which serves as a benchmark for the adequate intake (AI) of water and estimated energy requirements (EER). The objective of the current study was to examine the association of WT and TEE with physical activity and body composition in Japanese preschool children. We included 41 preschool children (22 girls, 19 boys) aged 3–6 in this study. WT, TEE, and fat-free mass (FFM) were obtained using DLW. Physical activity was measured using a triaxial accelerometer and categorized as light (LPA; 1.5–2.9 Metabolic equivalents, METs) and of moderate-to-vigorous intensity (MVPA; ≥3.0 METs). Exercise duration (Ex) was defined as ≥4.0 METs of physical activity. WT and TEE moderately positively correlated with Ex, but not with LPA. WT moderately positively correlated with BW and FFM while TEE strongly. We established predictive equations for WT and TEE using body weight (BW), FFM, step count, and Ex to guide the AI of water and EER in Japanese preschool children. We found that FFM and step count are the determinants of TEE, and that BW and Ex are the determinants of WT in preschool children.

Total Energy Expenditure, Body Composition, Physical Activity, and Step Count in Japanese Preschool Children: A Study Based on Doubly Labeled Water

Adequate energy intake is essential for the healthy development of children, and the estimated energy requirement of children is determined by total daily energy expenditure (TDEE) and energy deposition for growth. A previous study in Japanese tweens indicated that TDEE could be estimated by fat-free mass (FFM) and step count. The aims of this study were to measure TDEE in Japanese preschool children and to confirm whether TDEE can be estimated by FFM and step count in preschool children. Twenty-one children aged 4-6 years old (11 girls and 10 boys; age, 5.1 (0.9) years; height, 107.2 (6.6) cm; weight, 17.5 (1.7) kg; BMI, 15.3 (1.3); mean (SD)) participated in this study. FFM and 7-day TDEE were obtained by doubly labeled water (DLW). Participants wore accelerometers during the DLW measurement period. No significant differences were observed in age-adjusted height, weight, BMI, FFM (13.0 (1.5) kg), or TDEE (1300 (174) kcal/day) between girls and boys. Girls had significantly higher percent fat and a lower daily step count than boys. Stepwise regression analysis revealed that FFM and step count were significant predictors of TDEE; TDEE (kcal/day) = 85.0 × FFM (kg) + 0.0135 × step count (steps/day). This accounted for 74% of TDEE variance. The current study confirmed that FFM and step count are major determinants of TDEE in Japanese preschool children as well as adolescents, although further research is needed to obtain precise equations.

A Method to Detect Type 1 Diabetes Based on Physical Activity Measurements Using a Mobile Device

Type 1 diabetes is a chronic disease marked by high blood glucose levels, called hyperglycemia. Diagnosis of diabetes typically requires one or more blood tests. The aim of this paper is to discuss a non-invasive method of type 1 diabetes detection, based on physical activity measurement. We solved a binary classification problem using a variety of computational intelligence methods, including non-linear classification algorithms, which were applied and comparatively assessed. Prediction of disease presence among children and adolescents was evaluated using performance measures, such as accuracy, sensitivity, specificity, precision, the goodness index, and AUC. The most satisfying results were obtained when using the random forest method. The primary parameters in disease detection were weekly step count and the weekly number of vigorous activity minutes. The dependance between the weekly number of steps and the type 1 diabetes presence was established after an insightful analysis of data using classification and clustering algorithms. The findings have shown promising results that type 1 diabetes can be diagnosed using physical activity measurement. This is essential regarding the non-invasiveness and flexibility of the detection method, which can be tested at any time anywhere. The proposed technique can be implemented on a mobile device.