Total energy expenditure (EE) can be assessed in children by the heart-rate (HR) monitoring technique calibrated against open-circuit indirect calorimetry (IC). In this technique, sleeping EE is usually estimated as the lowest value of a 30 min resting EE measurement×0·90 to give an average for the total sleeping period. However, sleeping is a dynamic process in which sleeping EE is modulated by the effect of factors such as body movement and different sleep stages. The aim of the present study was to determine a new method to improve the sleeping EE measurement by taking into account body movements during sleep. Twenty-four non-obese children participated in the present study. All subjects passed through a calibration period. HR and EE measured by IC were simultaneously collected during resting, the postprandial period, and during different levels of activity. Different methods for computing sleeping EE (resting EE×0·90 with different breakpoints (‘flex point’ HR with linear regression or ‘inflection point’ (IP) HR with the third order polynomial regression equation (P3)) were compared with EE measured for least 2·0 h in eight sleeping children. The best method of calculation was then tested in sixteen children undergoing HR monitoring and with a body movement detector. In a subset of eight children undergoing simultaneous sleeping EE measurement by IC and HR, the use of the equation resting EE×0·8 when HR<IP and P3 when HR>IP during the sleeping period gave the lowest difference (1 (SD 5·4) %) compared with other methods (linear or polynomial regressions). The new formula was tested in an independent subset of sixteen other children. The difference between sleeping ee computed with the formula resting EE×0·90 and sleeping EE computed with resting EE×0·8 when HR<IP and the P3 equation when HR>IP during sleeping periods was significant (13 (sd 5·9)%) only for active sleeping subjects (n 6 of 16 subjects). The correlation between the difference in the results from the two methods of calculation and body movements was close (r 0·63, P<0·005, Spearman test) as well as computed sleeping EE (Spearman test, r 0·679, P<0·001), indicating that this new method is reliable for computing sleeping EE with HR monitoring if children are moving during sleep and improves the total EE assessment.

The fasting concentration of non-esterified fatty acids (NEFA) and the degree to which it declines during an oral glucose tolerance test are closely associated with insulin resistance and glucose intolerance. However, relatively few studies have described possible environmental determinants of NEFA concentrations. Physical activity is likely to be related to NEFA levels, but habitual activity level is difficult to quantify in epidemiological studies. In particular, it is unclear whether NEFA is more closely related to cardio-respiratory fitness or to habitual energy expenditure. In order to quantify these relationships, we analysed data from the Ely prospective population-based study in which 931 subjects underwent a glucose tolerance test with measurements of cardio-respiratory fitness and 4 d energy expenditure by heart-rate monitoring, a technique previously validated against whole-body calorimetry and doubly-labelled water. In order to estimate the latent variables of usual fitness and energy expenditure, a subset of 190 subjects underwent repeat testing on three further occasions over 1 year. In analyses adjusting only for age and sex, energy expenditure and cardio-respiratory fitness were both negatively correlated with the total area under the NEFA curve following the oral glucose load (standardised β coefficients -0·030 and -0·039 respectively; both P<0·001) However, further adjustment for degree of obesity and bivariate measurement error suggested that the effect of energy expenditure was significantly greater than that for fitness (-0·047 and -0·005 respectively). These results suggest that the area under the NEFA curve in the oral glucose tolerance test, a measure of insulin sensitivity, is strongly associated with the habitual level of physical activity.

Determining total energy expenditure (EE) in children under free-living conditions has become of increasingly clinical interest. The aim of this study was to compare three different methods to assess EE triaxial accelerometry (TriTrac-R3D; Professional Products, Division of Reining International, Madison, WI, USA), activity diary and heart-rate (HR) monitoring combined with indirect calorimetry (IC). Twenty non-obese children and adolescents, aged 5.5 to 16.0 years, participated in this study. Results from the three methods were collected simultaneously under free-living conditions during the same 24 h schoolday period. Neither activity diary (5904 (SD 1756) KJ) NOR THE TRITRAC-R3D (6389 (sd 979) kJ) showed statistical differences in 24 h total EE compared with HR monitoring (5965 (sd 1911) kJ). When considering different physical activity (PA) periods, compared with HR monitoring, activity diary underestimates total EE during sedentary periods (P<0·001) and overestimates total EE and PA-EE during PA periods (P<0·001) because of the high energy cost equivalence of activity levels. The TriTrac-R3D, compared with HR monitoring, shows good agreement for assessing PA-EE during PA periods (mean difference +0·25 (sd 1·9) kJ/min; 95 % CI for the bias -0·08, 0·58), but underestimates PA-EE and it does not show good precision during sedentary periods (-0·87 (sd 1·4) kJ/min, P<0·001). Correlation between the vector magnitude generated by the TriTrac-R3D accelerometer and EE of activities derived from HR monitoring is high. When compared with the HR method, the TriTrac-R3D and activity diary are not systematically accurate and must be carefully used for the assessment of children's EE depending on the purpose of each study.

Free-living energy expenditure was estimated by doubly-labelled water (DLW) and continuous heart-rate (HR) monitoring over nine consecutive days in nine healthy men with sedentary occupations but different levels of leisure-time physical activity. Individual calibrations of the HR-energy expenditure (EE) relationship were obtained for each subject using 30 min average values of HR and EE obtained during 24h whole-body calorimetry with a defined exercise protocol, and additional data points for individual leisure activities measured with an Oxylog portable O2 consumption meter. The HR data were processed to remove spurious values and insert missing data before the calculation of EE from second-order polynomial equations relating EE to HR. After data processing, the HR-derived EE for this group of subjects was on average 0.8 (sem 0.6) MJ/d, or 6.0 (sem 4.2)% higher than that estimated by DLW. The diary-respirometer method, used over the same 9d, gave values which were 1.9 (sem 0.7) MJ/d, or -12.1 (sem 4.0)% lower than the DLW method. The results suggest that HR monitoring can provide a better estimate of 24 h EE of groups than the diary-respirometer method, but show that both methods can introduce errors of 20% or more in individuals.