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Introduction: Digital technologies have enabled new possibilities to assess dietary intake and have shown promise in terms of decreased participant burden, improved accuracy and lower costs. However, their potential and validity in pregnant populations are scarcely explored., Objectives: This study aimed to (a) validate energy intakes obtained from a web-based dietary recall method developed for national surveys (RiksmatenFlex) against total energy expenditure (TEE) by means of the doubly labelled water (DLW) method, and (b) to compare intakes of macronutrients, key unhealthy and healthy foods as well as adherence to food-based dietary guidelines between RiksmatenFlex and repeated 24 h telephone dietary recalls in healthy Swedish pregnant women., Methods: This study was conducted as a nested validation within the HealthyMoms trial. Intakes of foods, macronutrients and energy were assessed during three days through RiksmatenFlex and 24 h telephone dietary recalls, and Swedish Healthy Eating Index (SHEI) scores were also calculated for both methods (n = 52). For 24 women, TEE was also assessed through the DLW method. Paired Samples T-tests and Wilcoxon Signed Ranks Tests were used to identify differences between means for foods, macronutrients, energy and SHEI scores. Pearson correlation coefficient or Spearman's rho were performed to identify relationships between variables. To compare energy intake (RiksmatenFlex) with TEE (DLW method) and 24 h telephone dietary recalls, Bland and Altman plots were constructed., Results: Average energy intake from RiksmatenFlex (10,015 [SD 2004] kJ) was not statistically different from TEE (10,252 [SD 1197] kJ) (p = 0.596) (mean difference: -237 kJ/24 h). Correspondingly, there were small mean differences between average intakes of key unhealthy and healthy foods and average SHEI scores between RiksmatenFlex and 24 h telephone dietary recalls. However, the Bland and Altman plots showed wide limits of agreement for all dietary variables (e.g., for energy intake using RiksmatenFlex versus TEE: ±4239 kJ/24 h). High correlations between the investigated dietary variables for the two dietary methods were observed (r = 0.751 to 0.931; all p < 0.001)., Conclusion: RiksmatenFlex captured average intakes of energy, unhealthy and healthy food groups and adherence to food-based dietary guidelines in a comparable way to 24 h telephone dietary recalls and the DLW method. Our results support the validity of RiksmatenFlex as a web-based dietary assessment method for future use in pregnancy for intervention studies and national dietary surveys., (© 2024. The Author(s).)

Objective: This study aimed to describe the energy expenditure (EE) and macronutrient oxidation response to an individualized nonshivering cold exposure in young healthy adults., Methods: Two different groups of 44 (study 1: 22.1  [SD 2.1] years old, 25.6  [SD 5.2] kg/m 2 , 34% men) and 13 young healthy adults (study 2: 25.6  [SD 3.0] years old, 23.6  [SD 2.4] kg/m 2 , 54% men) participated in this study. Resting metabolic rate (RMR) and macronutrient oxidation rates were measured by indirect calorimetry under fasting conditions in a warm environment (for 30 minutes) and in mild cold conditions (for 65 minutes, with the individual wearing a water-perfused cooling vest set at an individualized temperature adjusted to the individual's shivering threshold)., Results: In study 1, EE increased in the initial stage of cold exposure and remained stable for the whole cold exposure (P < 0.001). Mean cold-induced thermogenesis (9.56 ± 7.9 kcal/h) was 13.9% ± 11.6% of the RMR (range: -14.8% to 39.9% of the RMR). Carbohydrate oxidation decreased during the first 30 minutes of the cold exposure and later recovered up to the baseline values (P < 0.01) in parallel to opposite changes in fat oxidation (P < 0.01). Results were replicated in study 2., Conclusions: A 1-hour mild cold exposure individually adjusted to elicit maximum nonshivering thermogenesis induces a very modest increase in EE and a shift of macronutrient oxidation that may underlie a shift in thermogenic tissue activity., (© 2020 The Obesity Society.)

The time spent in sedentary behaviour represents an important public health burden. To reduce sedentary time in the general population, the simplest, most effective, and most accessible method is to decrease lying and sitting time. We aimed to compare differences on energy expenditure (EE) across lying, sitting, and standing positions; and to analyse the associations between the change on EE of changing from one position to another and anthropometric and body composition parameters in young healthy adults. A total of 55 (69% women) young healthy adults aged 21.7 ± 2.2 participated in the study. We measured EE by indirect calorimetry across lying, sitting, and standing positions following the standard procedures. The EE was significantly higher in standing than in both lying and sitting positions (mean difference: 0.121±0.292 and 0.125±0.241 kcal/min, respectively; all P<0.001), and no differences were observed between lying and sitting positions (P = 1.000). There was a negative association between the EE differences in sitting vs. standing position and lean body mass (P = 0.048), yet no associations between EE differences with the rest of the anthropometric and body composition parameters were observed in each position pair studied (all P>0.321). Our findings support the fact that increasing the time spent standing could be a simple strategy to slightly increase EE. Therefore, our results have important clinical implications including a better monitoring, characterizing, and promoting countermeasures to sedentariness through low-level physical activities., Competing Interests: The authors have declared that no competing interests exist.

Purpose: To assess the capacity of different acceleration metrics from wrist accelerations to estimate total energy expenditure (TEE) and activity energy expenditure (AEE) using doubly labeled water in preschool children., Methods: Thirty-nine preschoolers (5.5 ± 0.1 yr) were included. Total energy expenditure was measured using doubly labeled water during 14 d, and AEE was then calculated using a predicted basal metabolic rate. Participants wore a wGT3X-BT accelerometer on their nondominant wrist for ≥5 d. We derived the following metrics from raw accelerations: raw ActiGraph activity counts using the normal filter and the low-frequency extension; and alternate summary metrics, such as the Euclidian norm minus 1g (ENMO), Euclidian norm of the high-pass-filtered accelerations (HFEN), the bandpass-filtered accelerations, the HFEN plus Euclidean norm of low-pass filtered accelerations minus 1g (HFEN+) and the mean amplitude deviation., Results: Alternate summary metrics explained a larger proportion of the variance in TEE and AEE than ActiGraph's activity counts (counts, 7-8 and 25% of TEE and AEE; alternate summary metrics, 13%-16% and 35%-39% of TEE and AEE). Adjustments for body weight and height resulted in an explanation of 51% of AEE by ENMO. All of the metrics adjusted for fat mass and fat-free mass explained up to 84% and 67% of TEE and AEE, respectively., Conclusions: ENMO and the other alternate summary metrics explained more of the variance in TEE and AEE than the ActiGraph's activity counts in 5-yr-old children, suggesting further exploration of these variables in studies on physical activity and energy expenditure in preschoolers. Our results need confirmation in other populations with wider age groups and varying body compositions.

Having valid and reliable resting energy expenditure (REE) estimations is crucial to establish reachable goals for dietary and exercise interventions. However, most of the REE predictive equations were developed some time ago and, as the body composition of the current population has changed, it is highly relevant to assess the validity of REE predictive equations in contemporary young adults. In addition, little is known about the role of sex and weight status on the validity of these predictive equations. Therefore, this study aimed to investigate the role of sex and weight status in congruent validity of REE predictive equations in young adults. A total of 132 young healthy adults (67.4% women, 18⁻26 years old) participated in the study. We measured REE by indirect calorimetry strictly following the standard procedures, and we compared it to 45 predictive equations. The most accurate equations were the following: (i) the Schofield and the "Food and Agriculture Organization of the United Nations/World Health Organization/United Nations" (FAO/WHO/UNU) equations in normal weight men; (ii) the Mifflin and FAO/WHO/UNU equations in normal weight women; (iii) the Livingston and Korth equations in overweight men; (iv) the Johnstone and Frankenfield equations in overweight women; (v) the Owen and Bernstein equations in obese men; and (vi) the Owen equation in obese women. In conclusion, the results of this study show that the best equation to estimate REE depends on sex and weight status in young healthy adults.

Background and objectives : There is a lack of studies investigating associations of physical activity level (PAL) and activity energy expenditure (AEE) using the doubly-labeled water (DLW) method with body composition and physical fitness in young children. Thus, we aimed to examine cross-sectional associations of PAL and AEE with body composition indices and physical fitness components in Swedish preschool children. Materials and methods: PAL was calculated as total energy expenditure measured using DLW divided by the predicted basal metabolic rate in 40 children aged 5.5 (standard deviation 0.2) years. AEE was calculated as total energy expenditure minus basal metabolic rate and the thermic effect of food, and divided by fat-free mass. Body composition was assessed using the 3-component model by combining measurements based on isotope dilution and air-displacement plethysmography. Physical fitness (muscular strength, motor fitness, and cardiorespiratory fitness) was evaluated using the PREFIT test battery. Multiple linear regression models were conducted. Results: PAL and AEE were negatively associated with body mass index, percent body fat, and fat mass index (PAL: standardized β -0.35, -0.41, and -0.45, all p < 0.036; AEE: standardized β -0.44, -0.44, and -0.47, all p < 0.006, respectively). Furthermore, PAL and AEE were positively associated with the standing long jump test (PAL: standardized β 0.37, p = 0.017; AEE: standardized β 0.38, p = 0.014). There were no statistically significant associations found regarding PAL or AEE with fat-free mass index or any other physical fitness test. Conclusions: Greater PAL and AEE at the age 5.5 were significantly associated with body fatness and improved lower-body muscular strength. Therefore, increasing physical activity, and thus energy expenditure, at young ages may be beneficial for preventing overweight/obesity. However, further studies with larger sample sizes are needed to confirm the results.

Background/objectives: Easy-to-use and accurate methods to assess free-living activity energy expenditure (AEE) in preschool children are required. The aims of this study in healthy preschool children were to (a) evaluate the ability of the wrist-worn ActiGraph wGT3x-BT to predict free-living AEE and (b) assess wear compliance using a 7-day, 24-h protocol., Subjects/methods: Participants were 40 Swedish children (5.5±0.2 years) in the Mobile-based intervention intended to stop obesity in preschoolers (MINISTOP) obesity prevention trial. Total energy expenditure (TEE) was assessed using the doubly labeled water method during 14 days. AEE was calculated as (TEEx0.9) minus predicted basal metabolic rate. The ActiGraph accelerometer was worn on the wrist for 7 days and outputs used were mean of the daily and awake filtered vector magnitude (mean VM total and mean VM waking)., Results: The ActiGraph was worn for 7 (n=34, 85%), 6 (n=4, 10%), 5 (n=1, 2.5%) and 4 (n=1, 2.5%) days (a valid day was ⩾600 awake minutes). Alone, mean VM total and mean VM waking were able to explain 14% (P=0.009) and 24% (P=0.001) of the variation in AEE, respectively. By incorporating fat and fat-free mass in the models 58% (mean VM total) and 62% (mean VM waking) in the variation of AEE was explained (P<0.001)., Conclusions: The wrist-worn ActiGraph wGT3x-BT in combination with body composition variables explained up to the 62% of the variation in AEE. Given the high wear compliance, the wrist-worn ActiGraph has the potential to provide useful information in studies where physical activity in preschool children is measured.

Background: Accelerometers are widely used to measure sedentary time, physical activity, physical activity energy expenditure (PAEE), and sleep-related behaviors, with the ActiGraph being the most frequently used brand by researchers. However, data collection and processing criteria have evolved in a myriad of ways out of the need to answer unique research questions; as a result there is no consensus., Objectives: The purpose of this review was to: (1) compile and classify existing studies assessing sedentary time, physical activity, energy expenditure, or sleep using the ActiGraph GT3X/+ through data collection and processing criteria to improve data comparability and (2) review data collection and processing criteria when using GT3X/+ and provide age-specific practical considerations based on the validation/calibration studies identified., Methods: Two independent researchers conducted the search in PubMed and Web of Science. We included all original studies in which the GT3X/+ was used in laboratory, controlled, or free-living conditions published from 1 January 2010 to the 31 December 2015., Results: The present systematic review provides key information about the following data collection and processing criteria: placement, sampling frequency, filter, epoch length, non-wear-time, what constitutes a valid day and a valid week, cut-points for sedentary time and physical activity intensity classification, and algorithms to estimate PAEE and sleep-related behaviors. The information is organized by age group, since criteria are usually age-specific., Conclusion: This review will help researchers and practitioners to make better decisions before (i.e., device placement and sampling frequency) and after (i.e., data processing criteria) data collection using the GT3X/+ accelerometer, in order to obtain more valid and comparable data., Prospero Registration Number: CRD42016039991.

Accurate and easy-to-use methods to assess free-living energy expenditure in response to physical activity in young children are scarce. In the present study, we evaluated the capacity of (1) 4 d recordings obtained using the Actiheart (mean heart rate (mHR) and mean activity counts (mAC)) to provide assessments of total energy expenditure (TEE) and activity energy expenditure (AEE) and (2) a 7 d activity diary to provide assessments of physical activity levels (PAL) using three sets of metabolic equivalent (MET) values (PALTorun, PALAdolph and PALAinsworth) in forty-four and thirty-one healthy Swedish children aged 1·5 and 3 years, respectively. Reference TEE, PALref and AEE were measured using criterion methods, i.e. the doubly labelled water method and indirect calorimetry. At 1·5 years of age, mHR explained 8 % (P= 0·006) of the variation in TEE above that explained by fat mass and fat-free mass. At 3 years of age, mHR and mAC explained 8 (P= 0·004) and 6 (P= 0·03) % of the variation in TEE and AEE, respectively, above that explained by fat mass and fat-free mass. At 1·5 and 3 years of age, average PALAinsworth values were 1·44 and 1·59, respectively, and not significantly different from PALref values (1·39 and 1·61, respectively). By contrast, average PALTorun (1·5 and 3 years) and PALAdolph (3 years) values were lower (P< 0·05) than the corresponding PALref values. In conclusion, at both ages, Actiheart recordings explained a small but significant fraction of free-living energy expenditure above that explained by body composition variables, and our activity diary produced mean PAL values in agreement with reference values when using MET values published by Ainsworth.

Background: The prevalence of childhood overweight and obesity has increased recently, but the mechanisms involved are incompletely known. Previous research has shown a correlation between the percentage of total body fat (TBF) and physical activity level (PAL). However, the PAL values used may involve a risk of spurious correlations because they are often based on predicted rather than measured estimates of resting energy metabolism., Objectives: We studied the development of body composition during early childhood and the relation between the percentage of TBF and PAL on the basis of the measured resting energy metabolism., Design: Body composition was previously measured in 108 children when they were 1 and 12 wk old. When 44 of these children (21 girls and 23 boys) were 1.5 y old, their total energy expenditure and TBF were assessed by using the doubly labeled water method. Resting energy metabolism, which was assessed by using indirect calorimetry, was used to calculate PAL., Results: Significant correlations were shown for TBF (r = 0.32, P = 0.035) and fat-free mass (r = 0.34, P = 0.025) between values (kg) assessed at 12 wk and 1.5 y of age. For TBF (kg) a significant interaction (P = 0.035) indicated a possible sex difference. PAL at 1.5 y was negatively correlated with the percentage of TBF (r = -0.40, P = 0.0076) and the increase in the percentage of TBF between 12 wk and 1.5 y (r = -0.38, P = 0.0105)., Conclusions: The results indicate that body fatness and physical activity interact during early childhood and thereby influence obesity risk. Our results are based on a small sample, but nevertheless, they motivate additional studies in boys compared with girls regarding the development of body composition during early life.

Background/objectives: Energy costs of pregnancy approximate 320  MJ in well-nourished women, but whether or not these costs may be partly covered by modifications in activity behavior is incompletely known. In healthy Swedish women: (1) to evaluate the potential of the Intelligent Device for Energy Expenditure and Physical Activity (IDEEA) to assess energy expenditure during free-living conditions, (2) to assess activity pattern, walking pace and energy metabolism in pregnant women and non-pregnant controls, and (3) to assess the effect on energy expenditure caused by changes in physical activity induced by pregnancy., Subjects/methods: Activity pattern was assessed using the IDEEA in 18 women in gestational week 32 and in 21 non-pregnant women. Activity energy expenditure (AEE) was assessed using IDEEA, as well as using the doubly labelled water method and indirect calorimetry., Results: AEE using the IDEEA was correlated with reference estimates in both groups (r=0.4-0.5; P<0.05). Reference AEE was 0.9 MJ/24 h lower in pregnant than in non-pregnant women. Pregnant women spent 92 min/24 h more on sitting, lying, reclining and sleeping (P=0.020), 73 min/24 h less on standing (P=0.037) and 21 min/24 h less on walking and using stairs (P=0.049), and walked at a slower pace (1.1 ± 0.1 m/s versus 1.2±0.1 m/s; P=0.014) than did non-pregnant controls. The selection of less demanding activities and slower walking pace decreased energy costs by 720 kJ/24 h and 80 kJ/24 h, respectively., Conclusion: Healthy moderately active Swedish women compensated for the increased energy costs of pregnancy by 0.9 MJ/24 h. The compensation was mainly achieved by selecting less demanding activities.

Background: Physical activity promotes health and longevity. Further elaboration of the role of physical activity for human health in epidemiological studies on large samples requires accurate methods that are easy to use, cheap, and possible to repeat. The use of telecommunication technologies such as cell phones is highly interesting in this respect. In an earlier report, we showed that physical activity level (PAL) assessed using a cell phone procedure agreed well with corresponding estimates obtained using the doubly labeled water method. However, our earlier study indicated high within-subject variation in relation to between-subject variations in PAL using cell phones, but we could not assess if this was a true variation of PAL or an artifact of the cell phone technique., Objective: Our objective was to compare within- and between-subject variations in PAL by means of cell phones with corresponding estimates using an accelerometer. In addition, we compared the agreement of daily PAL values obtained using the cell phone questionnaire with corresponding data obtained using an accelerometer., Methods: PAL was measured both with the cell phone questionnaire and with a triaxial accelerometer daily during a 2-week study period in 21 healthy Swedish women (20 to 45 years of age and BMI from 17.7 kg/m² to 33.6 kg/m²). The results were evaluated by fitting linear mixed effect models and descriptive statistics and graphs., Results: With the accelerometer, 57% (95% confidence interval [CI] 40%-66%) of the variation was within subjects, while with the cell phone, within-subject variation was 76% (95% CI 59%-83%). The day-to-day variations in PAL observed using the cell phone questions agreed well with the corresponding accelerometer results., Conclusions: Both the cell phone questionnaire and the accelerometer showed high within-subject variations. Furthermore, day-to-day variations in PAL within subjects assessed using the cell phone agreed well with corresponding accelerometer values. Consequently, our cell phone questionnaire is a promising tool for assessing levels of physical activity. The tool may be useful for large-scale prospective studies.

Background: Few studies have compared the validity of objective measures of physical activity energy expenditure (PAEE) in pregnant and non-pregnant women. PAEE is commonly estimated with accelerometers attached to the hip or waist, but little is known about the validity and participant acceptability of wrist attachment. The objectives of the current study were to assess the validity of a simple summary measure derived from a wrist-worn accelerometer (GENEA, Unilever Discover, UK) to estimate PAEE in pregnant and non-pregnant women, and to evaluate participant acceptability., Methods: Non-pregnant (N = 73) and pregnant (N = 35) Swedish women (aged 20-35 yrs) wore the accelerometer on their wrist for 10 days during which total energy expenditure (TEE) was assessed using doubly-labelled water. PAEE was calculated as 0.9×TEE-REE. British participants (N = 99; aged 22-65 yrs) wore accelerometers on their non-dominant wrist and hip for seven days and were asked to score the acceptability of monitor placement (scored 1 [least] through 10 [most] acceptable)., Results: There was no significant correlation between body weight and PAEE. In non-pregnant women, acceleration explained 24% of the variation in PAEE, which decreased to 19% in leave-one-out cross-validation. In pregnant women, acceleration explained 11% of the variation in PAEE, which was not significant in leave-one-out cross-validation. Median (IQR) acceptability of wrist and hip placement was 9(8-10) and 9(7-10), respectively; there was a within-individual difference of 0.47 (p<.001)., Conclusions: A simple summary measure derived from a wrist-worn tri-axial accelerometer adds significantly to the prediction of energy expenditure in non-pregnant women and is scored acceptable by participants.

Healthy human pregnancy is associated with changes in food intake, body fatness, energy expenditure and insulin resistance. However, available knowledge is limited regarding the physiological basis of these changes. Published evidence suggests that so-called adipokines (i.e. leptin, adiponectin and resistin) have significant roles when such changes are established. We explored, throughout a complete pregnancy, relationships between total body fat (TBF), energy expenditure, insulin resistance (homeostasic model of insulin resistance, HOMA-IR) and serum concentrations of leptin, adiponectin and resistin. Such concentrations were assessed before pregnancy in gestational weeks 8, 14, 20, 32 and 35, and 2 weeks postpartum in twenty-three healthy women. TBF, BMR (n 23) and HOMA-IR (n 17) were assessed before pregnancy in gestational weeks 14 and 32 and 2 weeks postpartum. TBF (%) was correlated with HOMA-IR (r 0.68-0.79, P < 0.01) and with serum leptin (r 0.85-0.88, P < 0.001) before and during pregnancy. Serum leptin was correlated with HOMA-IR (r 0.53-0.70, P < 0.05) before and during pregnancy. Serum adiponectin was inversely correlated with HOMA-IR in gestational week 32 (r - 0.52, P < 0.05). When HOMA-IR was regressed on TBF (%), the slope of the regression line was 0.046 before pregnancy, which was significantly (P < 0.05) different from the corresponding value, 0.111, in gestational week 32. The results indicate that pregnancy has an enhancing effect on the relationship between body fatness and insulin resistance. This effect, possibly mediated by leptin, may represent a mechanism by which offspring size is regulated in response to the nutritional situation of the mother.

This review summarizes information regarding how human energy metabolism is affected by pregnancy, and current estimates of energy requirements during pregnancy are presented. Such estimates can be calculated using either increases in basal metabolic rate (BMR) or increases in total energy expenditure (TEE). The two modes of calculation give similar results for a complete pregnancy but different distributions of energy requirements in the three trimesters. Recent information is presented regarding the effect of pregnancy on BMR, TEE, diet-induced thermogenesis, and physical activity. The validity of energy intake (EI) data recently assessed in well-nourished pregnant women was evaluated using information regarding energy metabolism during pregnancy. The results show that underreporting of EI is common during pregnancy and indicate that additional longitudinal studies, taking the total energy budget during pregnancy into account, are needed to satisfactorily define energy requirements during the three trimesters of gestation.

Purpose: Interpretation of physical activity as energy expenditure requires adjustment for body size. This is usually accomplished by means of the MET system, which assumes a basal metabolic rate (BMR) of 4.184 kJ.kg(-1).h(-1) and, when the standard calculation is used, that the energy costs of different activities are proportional to BMR. These relationships may be altered by increases in percent total body fat (% TBF), and this paper examines relationships between % TBF and total energy expenditure (TEE) obtained using the standard and a proposed calculation., Methods: Published data regarding body composition, physical activity (heart rate recording), and BMR in 11 American women (before and after weight gain) and in 15 Swedish women (before and during pregnancy) were used to calculate TEE. Reference estimates of TEE were obtained using doubly labeled water., Results: In Swedish women, reference TEE minus standard TEE (MJ.24 h(-1)) was 1.37 +/- 1.29 (P < 0.01) before and 1.03 +/- 1.13 (P < 0.05) during pregnancy. For proposed TEE these differences were 0.43 +/- 1.63 (P > 0.05) and 0.31 +/- 1.28 (P > 0.05) (MJ.24 h(-1)), respectively. In American women before and after weight gain, reference TEE minus standard TEE (MJ.24 h(-1)) were 0.38 +/- 1.79 (P > 0.05) and 1.39 +/- 2.36 (P > 0.05), respectively, whereas the corresponding differences for proposed TEE (MJ.24 h(-1)) were -0.52 +/- 2.20 (P > 0.05) and 0.21 +/- 2.36 (P > 0.05), respectively. In Swedish women before pregnancy and American women after weight gain (N = 26, BMI = 18-39), significant (P < 0.001) relationships were found for standard TEE/proposed TEE (y) versus % TBF, (x, r = -0.65) and versus BMI (x, r = -0.70)., Conclusions: : In individuals with a TBF content typical for contemporary Western women, standard TEE is lower than proposed TEE. This bias increases as the TBF content of subjects increases. The results indicate that proposed TEE is more accurate than standard TEE, but this requires confirmation.

Simple methods were developed and evaluated to assess total energy expenditure in 24 healthy, Swedish women planning pregnancy. Total energy expenditure was measured by the doubly-labeled water method (reference method) and three simple methods: heart rate recording, movement registration by an accelerometer, and a questionnaire. Mean total energy expenditure obtained by the four methods varied between 2,530 kcal per 24 hours (10,570 kJ/24 hours) and 2,730 kcal per 24 hours (11,420 kJ/24 hours). No significant difference between the results obtained by the different methods was found. The mean difference between the simple method and the reference method was for the questionnaire 204 +/- 508 kcal per 24 hours (853 +/- 2,124 kJ/24 hours), for the heart rate recorder 58 +/- 338 kcal per 24 hours (241 +/- 1,416 kJ/24 hours) and for the accelerometer 6 +/- 325 kcal per 24 hours (25 +/- 1,360 kJ/24 hours). The heart rate recorder and the questionnaire overestimated high and underestimated low energy expenditures. The accelerometer and the heart rate recorder were able to assess mean total energy expenditure of groups. No systematic bias was found when the accelerometer was used.