A lifetime of change: Measurements of total and basal energy in a large cohort of subjects at ages spanning from before birth to old age document distinct changes that occur during a human lifetime. Pontzer et al . report that energy expenditure (adjusted for weight) in neonates was like that of adults but increased substantially in the first year of life (see the Perspective by Rhoads and Anderson). It then gradually declined until young individuals reached adult characteristics, which were maintained from age 20 to 60 years. Older individuals showed reduced energy expenditure. Tissue metabolism thus appears not to be constant but rather to undergo transitions at critical junctures. —LBR

Objective: To investigate the ability of a newly developed triaxial accelerometer to predict total energy expenditure (EE) (TEE) and activity‐related EE (AEE) in free‐living conditions.Research Methods and Procedures: Subjects were 29 healthy subjects between the ages of 18 and 40. The Triaxial Accelerometer for Movement Registration (Tracmor) was worn for 15 consecutive days. Tracmor output was defined as activity counts per day (ACD) for the sum of all three axes or each axis separately (ACD‐X, ACD‐Y, ACD‐Z). TEE was measured with the doubly labeled water technique. Sleeping metabolic rate (SMR) was measured during an overnight stay in a respiration chamber. The physical activity level was calculated as TEE × SMR⁻¹, and AEE was calculated as [(0.9 × TEE) − SMR]. Body composition was calculated from body weight, body volume, and total body water using Siri's three‐compartment model.Results: Age, height, body mass, and ACD explained 83% of the variation in TEE [standard error of estimate (SEE) = 1.00 MJ/d] and 81% of the variation in AEE (SEE = 0.70 MJ/d). The partial correlations for ACD were 0.73 (p < 0.001) and 0.79 (p < 0.001) with TEE and AEE, respectively. When data on SMR or body composition were used with ACD, the explained variation in TEE was 90% (SEE = 0.74 and 0.77 MJ/d, respectively). The increase in the explained variation using three axes instead of one axis (vertical) was 5% (p < 0.05).Discussion: The correlations between Tracmor output and EE measures are the highest reported so far. To measure daily life activities, the use of triaxial accelerometry seems beneficial to uniaxial.

Water is essential for survival, but one in three individuals worldwide (2.2 billion people) lacks access to safe drinking water. Water intake requirements largely reflect water turnover (WT), the water used by the body each day. We investigated the determinants of human WT in 5604 people from the ages of 8 days to 96 years from 23 countries using isotope-tracking ( ² H) methods. Age, body size, and composition were significantly associated with WT, as were physical activity, athletic status, pregnancy, socioeconomic status, and environmental characteristics (latitude, altitude, air temperature, and humidity). People who lived in countries with a low human development index (HDI) had higher WT than people in high-HDI countries. On the basis of this extensive dataset, we provide equations to predict human WT in relation to anthropometric, economic, and environmental factors.

Health is associated with amount of daily physical activity. Recently, the identification of sedentary time as an independent factor, has gained interest. A valid and easy to use activity monitor is needed to objectively investigate the relationship between physical activity, sedentary time and health. We compared validity and reproducibility of physical activity measurement and posture identification of three activity monitors, as well as user friendliness. Healthy volunteers wore three activity monitors simultaneously: ActivPAL3, ActiGraphGT3X and CAM. Data were acquired under both controlled (n = 5) and free-living conditions (n = 9). The controlled laboratory measurement, that included standardized walking intensity and posture allocation, was performed twice. User friendliness was evaluated with a questionnaire. Posture classification was compared with direct observation (controlled measurement) and with diaries (free living). Accelerometer intensity accuracy was tested by correlations with walking speed. User friendliness was compared between activity monitors. Reproducibility was at least substantial in all monitors. The difference between the two CAM measurements increased with walking intensity. Amount of correct posture classification by ActivPAL3 was 100.0% (kappa 0.98), 33.9% by ActiGraphGT3X (kappa 0.29) and 100.0% by CAM (kappa 0.99). Correlations between accelerometer intensity and walking speed were 0.98 for ActivPAL3, 1.00 for ActiGraphGT3X and 0.98 for CAM. ICCs between activity monitors and diary were 0.98 in ActivPAL3, 0.59 and 0.96 in ActiGraphGT3X and 0.98 in CAM. ActivPAL3 and ActiGraphGT3X had higher user friendliness scores than the CAM. The ActivPAL3 is valid, reproducible and user friendly. The posture classification by the ActiGraphGT3X is not valid, but reflection of walking intensity and user friendliness are good. The CAM is valid; however, reproducibility at higher walking intensity and user friendliness might cause problems. Further validity studies in free living are recommended.

Objective: The impact of season on energy expenditure and physical activity is not well quantified. This study focused on summer‐winter differences in total energy expenditure (TEE) and physical activity.Research Methods and Procedures: Twenty‐five healthy Dutch young adults, living in an urban environment, were measured in the summer season and the winter season. TEE was measured using doubly labeled water, and sleeping metabolic rate (SMR) was measured during an overnight stay in a respiration chamber. Subsequently, the physical activity level (PAL = TEE/SMR) and activity‐related energy expenditure [(0.9 × TEE) − SMR] were calculated. Maximal mechanical power (W_max) was determined with an incremental test on a cycle ergometer. Body composition was measured with hydrostatic weighing and deuterium dilution using Siri's three‐compartment model.Results: There was no difference in TEE between seasons. PAL was higher in summer than in winter (1.87 ± 0.22 vs. 1.76 ± 0.18; p < 0.001), and the difference was higher for men than for women (0.20 ± 0.14 vs. 0.05 ± 0.16; p = 0.04). The difference in PAL between seasons was dependent on the initial activity level. There was a strong linear relation (R² = 0.48) between PAL and physical fitness (W_max/fat‐free mass), but W_max/fat‐free mass did not change between seasons in response to the lower PAL in winter.Discussion: The extent of the changes in PAL is of physiological significance, and seasonality in physical activity should be taken into account when studying physical activity patterns or relationships between physical activity and health.