Energy Expenditure Compared to Physical Activity Measured by Accelerometry and Self-Report in Adolescents: A Validation Study

Background

Physical inactivity is responsible for 5.3 million deaths annually worldwide. To measure physical activity energy expenditure, the doubly labeled water (DLW) method is the gold standard. However, questionnaires and accelerometry are more widely used. We compared physical activity measured by accelerometer and questionnaire against total (TEE) and physical activity energy expenditure (PAEE) estimated by DLW.

Methods

TEE, PAEE (TEE minus resting energy expenditure) and body composition were measured using the DLW technique in 25 adolescents (16 girls) aged 13 years living in Pelotas, Brazil. Physical activity was assessed using the Actigraph accelerometer and by self-report. Physical activity data from accelerometry and self-report were tested against energy expenditure data derived from the DLW method. Further, tests were done to assess the ability of moderate-to-vigorous intensity physical activity (MVPA) to predict variability in TEE and to what extent adjustment for fat and fat-free mass predicted the variability in TEE.

Results

TEE varied from 1,265 to 4,143 kcal/day. It was positively correlated with physical activity (counts) estimated by accelerometry (rho  = 0.57; p = 0.003) and with minutes per week of physical activity by questionnaire (rho  = 0.41; p = 0.04). An increase of 10 minutes per day in moderate-to-vigorous intensity physical activity (MVPA) relates to an increase in TEE of 141 kcal/day. PAEE was positively correlated with accelerometry (rho  = 0.64; p = 0.007), but not with minutes per week of physical activity estimated by questionnaire (rho  = 0.30; p = 0.15). Physical activity by accelerometry explained 31% of the vssariability in TEE. By incorporating fat and fat-free mass in the model, we were able to explain 58% of the variability in TEE.

Conclusion

Objectively measured physical activity significantly contributes to the explained variance in both TEE and PAEE in Brazilian youth. Independently, body composition also explains variance in TEE, and should ideally be taken into account when using accelerometry to predict energy expenditure values.     

Predicting Physical Activity Energy Expenditure Using Accelerometry in Adults From Sub‐Sahara Africa

Lack of physical activity may be an important etiological factor in the current epidemiological transition characterized by increasing prevalence of obesity and chronic diseases in sub‐Sahara Africa. However, there is a dearth of data on objectively measured physical activity energy expenditure (PAEE) in this region. We sought to develop regression equations using body composition and accelerometer counts to predict PAEE. We conducted a cross‐sectional study of 33 adult volunteers from an urban (n = 16) and a rural (n = 17) residential site in Cameroon. Energy expenditure was measured by doubly labeled water (DLW) over a period of seven consecutive days. Simultaneously, a hip‐mounted Actigraph accelerometer recorded body movement. PAEE prediction equations were derived using accelerometer counts, age, sex, and body composition variables, and cross‐validated by the jack‐knife method. The Bland and Altman limits of agreement (LOAs) approach was used to assess agreement. Our results show that PAEE (kJ/kg/day) was significantly and positively correlated with activity counts from the accelerometer (r = 0.37, P = 0.03). The derived equations explained 14–40% of the variance in PAEE. Age, sex, and accelerometer counts together explained 34% of the variance in PAEE, with accelerometer counts alone explaining 14%. The LOAs between DLW and the derived equations were wide, with predicted PAEE being up to 60 kJ/kg/day below or above the measured value. In summary, the derived equations performed better than existing published equations in predicting PAEE from accelerometer counts in this population. Accelerometry could be used to predict PAEE in this population and, therefore, has important applications for monitoring population levels of total physical activity patterns.     

Physical Activity Energy Expenditure of Adolescents in India

Physical activity (PA) has rarely been quantified in adolescent populations undergoing economic transition; therefore relationships with disease still remain uncertain. This study assessed whether absolute PA energy expenditure (PAEE), PAEE/kg, and PAEE/kg_FFM could be accurately estimated using accelerometry and a questionnaire in Indian adolescents and how these values compared to those of other populations. PAEE was assessed using doubly labeled water (DLW) in 30 adolescents from Chennai, India, over seven consecutive days, simultaneous with the measurement of PA using accelerometry and a previous‐week recall questionnaire. Accelerometry counts (regression analysis) and questionnaire data were used to estimate PAEE; estimates were cross‐validated using the Bland‐Altman method. Accelerometry data and DLW‐derived PAEE were visually compared to values from four North American and European populations. For boys, 49% of the variance in DLW‐derived PAEE was explained with an equation including accelerometry counts and fat‐free mass (FFM). Questionnaire‐derived estimates did not contribute to the explained variance in DLW derived PAEE. The group‐level PA of these Indian adolescents was successfully assessed using accelerometry, but not questionnaire. DLW‐derived PAEE/kg_FFM (mean (s.d.): 53.0 (27.5) kJ/kg_FFM/day) was lower in this group than other adolescent populations in Europe and similar to those in North America. Additionally, four boys and none of the girls accumulated ≥60 min/day of accelerometry‐derived moderate intensity activity, indicating low levels of PAEE and PA in these adolescents. Further research is necessary to investigate the association between PA and health outcomes in Indian adolescents.     

Resistance training increases total daily energy expenditure in disabled older women with coronary heart disease.

Physical activity energy expenditure (PAEE) is a determinant of prognosis and fitness in older patients with coronary heart disease (CHD). PAEE and total energy expenditure (TEE) are closely related to fatness, physical function, and metabolic risk in older individuals. The goal of this study was to assess effects of resistance training on PAEE, TEE, and fitness in older women with chronic CHD and physical activity limitations (N = 51, mean age: 72 + 5 yr). The study intervention consisted of a progressive, 6-mo program of resistance training vs. a control group condition of low-intensity yoga and deep breathing. The study interventions were completed by 42 of the 51 participants. The intervention group manifested a 177 +/- 213 kcal/day (+9%) increase in TEE, pre- to posttraining, measured by the doubly labeled water technique during a nonexercise 10-day period (P < 0.03 vs. controls). This was due to a 50 +/- 74 kcal/day (4%) increase in resting metabolic rate measured by indirect calorimetry (P < 0.01, P < 0.05 vs. controls) and a 123 +/- 214 kcal/day (9%) increase in PAEE (P < 0.03, P = 0.12 vs. controls). Resistance training was associated with significant increases in upper and lower body strength, but no change in fat-free mass, measured by dual X-ray absorptiometry, or left ventricular function, measured by echocardiography and Doppler. Women in the control group showed no alterations in TEE or its determinants. There were no changes between groups in body composition, aerobic capacity, or measures of mental depression. These results demonstrate that resistance training of 6-mo duration leads to an increase in TEE and PAEE in older women with chronic CHD.     

Ability of the Actiwatch Accelerometer to Predict Free‐Living Energy Expenditure in Young Children

Objective: To determine whether activity counts obtained with the Actiwatch monitor are associated with total expenditure and body composition in young children. Research Methods and Procedures: Actiwatch activity monitors were tested in 29 children 4 to 6 years old under field conditions over eight days. Total energy expenditure (TEE) was assessed with the doubly labeled water (DLW) technique. Correlation analyses were used to identify variables related to energy expenditure and percentage body fat. Multiple linear regression analyses were used to examine the variance in TEE and percentage body fat explained by activity counts after adjusting for relevant covariates. Results: Both average total daily activity counts (658, 816 ± 201, 657) and the pattern of activity were highly variable among subjects. TEE was significantly related to lean body mass (r = 0.45) and age (r = 0.48; p < 0.05 for both). Activity counts alone were not associated with TEE. In multiple linear regression analyses, TEE was independently associated with only lean body mass. Percentage fat mass was independently associated with body weight, being a girl, and being white, but not with average total activity counts. Discussion: Activity counts obtained with the Actiwatch under free‐living conditions do not reflect TEE in 4‐ to 6‐year‐old children and are not correlated with percentage fat mass. Therefore, average total activity counts obtained with the Actiwatch may be of limited value in identifying children at risk for becoming obese.     

Estimation of daily energy expenditure in pregnant and non-pregnant women using a wrist-worn tri-axial accelerometer

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.     

Estimation of Free‐Living Energy Expenditure Using a Novel Activity Monitor Designed to Minimize Obtrusiveness

The aim of this study was to investigate the ability of a novel activity monitor designed to be minimally obtrusive in predicting free‐living energy expenditure. Subjects were 18 men and 12 women (age: 41 ± 11 years, BMI: 24.4 ± 3 kg/m²). The habitual physical activity was monitored for 14 days using a DirectLife triaxial accelerometer for movement registration (Tracmor_D) (Philips New Wellness Solutions, Lifestyle Incubator, the Netherlands). Tracmor_D output was expressed as activity counts per day (Cnts/d). Simultaneously, total energy expenditure (TEE) was measured in free living conditions using doubly labeled water (DLW). Activity energy expenditure (AEE) and the physical activity level (PAL) were determined from TEE and sleeping metabolic rate (SMR). A multiple‐linear regression model predicted 76% of the variance in TEE, using as independent variables SMR (partial‐r² = 0.55, P < 0.001), and Cnts/d (partial r² = 0.21, P < 0.001). The s.e. of TEE estimates was 0.9 MJ/day or 7.4% of the average TEE. A model based on body mass (partial‐r² = 0.31, P < 0.001) and Cnts/d (partial‐r² = 0.23, P < 0.001) predicted 54% of the variance in TEE. Cnts/d were significantly and positively associated with AEE (r = 0.54, P < 0.01), PAL (r = 0.68, P < 0.001), and AEE corrected by body mass (r = 0.71, P < 0.001). This study showed that the Tracmor_D is a highly accurate instrument for predicting free‐living energy expenditure. The miniaturized design did not harm the ability of the instrument in measuring physical activity and in determining outcome parameters of physical activity such as TEE, AEE, and PAL.     

Physical activity-related energy expenditure with the RT3 and TriTrac accelerometers in overweight adults

Objective: The objective was to evaluate two accelerometers, the RT3 and the TriTrac‐R3D for their ability to produce estimates of physical activity‐related energy expenditure (PAEE) in overweight/obese adults. Research Methods and Procedures: PAEE estimates from both accelerometers were obtained in two experiments. In Experiment 1, 13 overweight/obese subjects (BMI 34.2 ± 6.4 kg/m²) were monitored over 2 weeks in everyday life, PAEE being simultaneously measured by the doubly labeled water method (DLW). In Experiment 2, 8 overweight/obese subjects (BMI 34.3 ± 5.0 kg/m²) and 10 normal‐weight subjects (BMI 20.8 ± 2.1 kg/m²) were monitored during a treadmill walking protocol, PAEE being simultaneously measured by indirect calorimetry. Results: In Experiment 1, there was no significant difference between methods in mean PAEE (DLW: 704 ± 223 kcal/d, RT3: 656 ± 140 kcal/d, TriTrac‐R3D 624 ± 419 kcal/d). The relative difference between methods (accelerometer vs. DLW) was ?17.1% ± 16.7% for the RT3 and ?20.0 ± 44.6% for the TriTrac‐R3D. Correlation for PAEE between RT3 and DLW was higher than between TriTrac‐R3D and DLW (r = 0.67, p < 0.05 and r = 0.36, p = 0.25, respectively). The 95% confidence interval (CI) (kcal/d) of the mean difference between methods was large, amounting to ?385 to 145 for the RT3 and ?887 to 590 for the TriTrac‐R3D. In Experiment 2, both accelerometers were sensitive to the changes in treadmill speed, with no significant difference in mean PAEE between methods in overweight/obese subjects. Conclusions: Although both accelerometers did not provide accurate estimates of PAEE at individual levels, the data suggest that RT3 has the potential to assess PAEE at group levels in overweight/obese subjects.     

Determinants of Free-Living Energy Expenditure in Normal Weight and Obese Women Measured by Doubly Labeled Water

Total free-living energy expenditure (TEE) was measured in 9 normal weight controls and 5 obese women using the doubly labeled water (DLW) method. Resting energy expenditure (REE) and the thermic effect of food (TEF) were measured by indirect calorimetry and the energy cost of physical activity (PA) calculated by deduction, in order to quantify the components and identify determinants of free-living TEE. Although REE was quantitatively the major component of TEE in both groups, PA best explained the variability, contributing 76% to the variance in free-living TEE. The obese women had elevated values for TEE (12397+/-2565 vs. 8339+/-1787 kJ/d, mean+/-SD; p<0.00S), compared with the control women. PA (5071+/-2385 vs. 2552+/-1452; p<0.0S) and REE (6393+/-678 vs. 5084+/-259; p<0.000S) were also raised in the obese, whereas TEF was not significantly different between the groups, accounting for 7.6% of energy expenditure for the obese and 8% for the control subjects. Body weight was the single best determinant of mean daily free-living TEE across both groups. We conclude that PA and body weight are the main determinants of free-living TEE .     

Differences in daily energy expenditure in lean and obese women: the role of posture allocation

Objective: A low resting metabolic rate (RMR) is considered a risk factor for weight gain and obesity; however, due to the greater fat‐free mass (FFM) found in obesity, detecting an impairment in RMR is difficult. The purposes of this study were to determine the RMR in lean and obese women controlling for FFM and investigate activity energy expenditure (AEE) and daily activity patterns in the two groups. Methods and Procedures: Twenty healthy, non‐smoking, pre‐menopausal women (10 lean and 10 obese) participated in this 14‐day observational study on free‐living energy balance. RMR was measured by indirect calorimetry; AEE and total energy expenditure (TEE) were calculated using doubly labeled water (DLW), and activity patterns were investigated using monitors. Body composition including FFM and fat mass (FM) was measured by dual energy X‐ray absorptiometry (DXA). Results: RMR was similar in the obese vs. lean women (1601 ± 109 vs. 1505 ± 109 kcal/day, respectively, P = 0.12, adjusting for FFM and FM). Obese women sat 2.5 h more each day (12.7 ± 3.2 h vs. 10.1 ± 2.0 h, P < 0.05), stood 2 h less (2.7 ± 1.0 h vs. 4.7 ± 2.2 h, P = 0.02) and spent half as much time in activity than lean women (2.6 ± 1.5 h vs. 5.4 ± 1.9 h, P = 0.002). Discussion: RMR was not lower in the obese women; however, they were more sedentary and expended less energy in activity than the lean women. If the obese women adopted the activity patterns of the lean women, including a modification of posture allocation, an additional 300 kcal could be expended every day.     

Effect of constitution on mass of individual organs and their association with metabolic rate in humans--a detailed view on allometric scaling

Resting energy expenditure (REE)-power relationships result from multiple underlying factors including weight and height. In addition, detailed body composition, including fat free mass (FFM) and its components, skeletal muscle mass and internal organs with high metabolic rates (i.e. brain, heart, liver, kidneys), are major determinants of REE. Since the mass of individual organs scales to height as well as to weight (and, thus, to constitution), the variance in these associations may also add to the variance in REE. Here we address body composition (measured by magnetic resonance imaging) and REE (assessed by indirect calorimetry) in a group of 330 healthy volunteers differing with respect to age (17-78 years), sex (61% female) and BMI (15.9-47.8 kg/m(2)). Using three dimensional data interpolation we found that the inter-individual variance related to scaling of organ mass to height and weight and, thus, the constitution-related variances in either FFM (model 1) or kidneys, muscle, brain and liver (model 2) explained up to 43% of the inter-individual variance in REE. These data are the first evidence that constitution adds to the complexity of REE. Since organs scale differently as weight as well as height the "fit" of organ masses within constitution should be considered as a further trait.     

Determinants of insulin-resistant phenotypes in normal-weight and obese Black African women

Objective: Subsets of metabolically “healthy obese” and “at‐risk” normal‐weight individuals have been previously identified. The aim of this study was to explore the determinants of these phenotypes in black South African (SA) women. Methods and Procedures: From a total of 103 normal‐weight (BMI ≤ 25 kg/m²) and 122 obese (BMI ≥ 30 kg/m²) black SA women, body composition, fat distribution, blood pressure, fasting glucose levels, insulin resistance, and lipid profiles were measured. Questionnaires relating to family history, physical activity energy expenditure (PAEE), and socio‐demographic variables were administered. The subjects were classified as insulin sensitive or insulin resistant according to the homeostasis model assessment of insulin resistance (HOMA‐IR) (≥1.95 insulin resistant). Results: Our study showed that 22% of the normal‐weight women were insulin resistant and 38% of the obese women were insulin sensitive. Increased visceral adipose tissue (VAT) (P = 0.001) and decreased VAT/leg fat mass (P ≤ 0.001), independent of total body fatness, distinguished between the phenotypes. Moreover, the insulin‐sensitive women were of higher socioeconomic status, did more leisure and vigorous PAEE and were less likely to use injectable contraceptives. Using a regression model, body fat distribution, percent body fat, age, log leisure PAEE, and use of injected contraception accounted for 35% of the variance in HOMA‐IR in the normal‐weight women. In the obese women, 34% of the variance in HOMA‐IR was explained by the same variables, excluding PAEE. No differences in smoking status or family history of metabolic disease were found between the phenotypes. Discussion: Central fat distribution, total adiposity, socioeconomic status, leisure PAEE, and use of injectable contraceptives distinguished between insulin‐sensitive and insulin‐resistant black SA women.     

Estimation of Free-Living Energy Expenditure by Heart Rate and Movement Sensing: A Doubly-Labelled Water Study

Background

Accurate assessment of energy expenditure (EE) is important for the study of energy balance and metabolic disorders. Combined heart rate (HR) and acceleration (ACC) sensing may increase precision of physical activity EE (PAEE) which is the most variable component of total EE (TEE).

Objective

To evaluate estimates of EE using ACC and HR data with or without individual calibration against doubly-labelled water (DLW) estimates of EE.

Design

23 women and 23 men (22–55 yrs, 48–104 kg, 8–46%body fat) underwent 45-min resting EE (REE) measurement and completed a 20-min treadmill test, an 8-min step test, and a 3-min walk test for individual calibration. ACC and HR were monitored and TEE measured over 14 days using DLW. Diet-induced thermogenesis (DIT) was calculated from food-frequency questionnaire. PAEE (TEE ÷ REE ÷ DIT) and TEE were compared to estimates from ACC and HR using bias, root mean square error (RMSE), and correlation statistics.

Results

Mean(SD) measured PAEE and TEE were 66(25) kJ·day^-1·kg^-1, and 12(2.6) MJ·day^-1, respectively. Estimated PAEE from ACC was 54(15) kJ·day^-1·kg^-1 (p<0.001), with RMSE 24 kJ·day^-1·kg^-1 and correlation r = 0.52. PAEE estimated from HR and ACC+HR with treadmill calibration were 67(42) and 69(25) kJ·day^-1·kg^-1 (bias non-significant), with RMSE 34 and 20 kJ·day^-1·kg^-1 and correlations r = 0.58 and r = 0.67, respectively. Similar results were obtained with step-calibrated and walk-calibrated models, whereas non-calibrated models were less precise (RMSE: 37 and 24 kJ·day^-1·kg^-1, r = 0.40 and r = 0.55). TEE models also had high validity, with biases <5%, and correlations r = 0.71 (ACC), r = 0.66–0.76 (HR), and r = 0.76–0.83 (ACC+HR).

Conclusions

Both accelerometry and heart rate may be used to estimate EE in adult European men and women, with improved precision if combined and if heart rate is individually calibrated.     

Measuring Free‐Living Energy Expenditure and Physical Activity with Triaxial Accelerometry

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.     

Kinetics of stable isotope and body composition in olive baboons (Papio anubis) estimated by deuterium dilution space: a pilot study

Morphometrics and isotope-labelled water were used to determine body composition [total body water, total body fat and fat-free mass (FFM)] of three captive female olive baboons (Papio anubis). Mean mass was 16.5 kg, comparable with other captive settings but heavier than wild olive baboons. Average water content was 66%; FFM averaged 90.5%. Baboon females have less body fat than human counterparts. Compared with captive or wild baboons, these females were adequately nourished for their energy expenditure. A positive association between total mass and FFM existed, but due to the small sample no general relationship was observed for body fat or FFM and condition or size measures. The kinetics of deuterium equilibration in body fluids for baboons was determined as 3-4 hours after injection, similar to that for humans. Deuterium dilution technique appears to be an appropriate method for studying body composition in baboons, although a larger sample is needed for relationships between morphometric indices and body composition.     

Segregation analysis of fat mass and other body composition measures derived from underwater weighing.

Segregation patterns of three body composition measures which were derived from underwater weighing were evaluated in a random sample of 176 French-Canadian families. Two of the variables can be considered as primary partitions of weight (fat mass [FM] and fat-free mass [FFM]), while the remaining variable (percent body fat [%BF]) is a derived index combining the measures of both fat and fat-free weight. This study represents the first report investigating major gene effects for these measures. Segregation analyses revealed that a major locus hypothesis could not be rejected for two of the three phenotypes. The single exception was FFM, for which nearly 60% of the variance was accounted for by a non-Mendelian major effect, which may reflect environmentally based commingling or may be in part a function of gene-environment interactions or correlations. In contrast to the results for FFM, the results for each of FM and %BF were similar and suggested a major locus which accounted for 45% of the variance, with an additional 22%-26% due to a multifactorial component. Given the similarity of the major gene characteristics for these two phenotypes, the possibility that the same gene underlies both measures warrants investigation. A reasonable hypothesis is to consider genes that may influence nutrient partitioning, as the family of candidate genes to receive the major attention.     

Resting energy expenditure-fat-free mass relationship: new insights provided by body composition modeling

The relationship between resting energy expenditure (REE) and metabolically active fat-free mass (FFM) is a cornerstone in the study of physiological aspects of body weight regulation and human energy requirements. Important questions, however, remain unanswered regarding the observed linear REE-FFM association in adult humans. This led us to develop a series of REE-body composition models that provide insights into the widely used simple linear REE-FFM prediction model derived experimentally in adult humans. The new models suggest that the REE-FFM relationship in mammals as a whole is curvilinear, that a segment of this function within a FFM range characteristic of adult humans can be fit with a linear equation almost identical to that observed from a composite review of earlier human studies, and that mammals as a whole exhibit a decrease in the proportion of FFM as high metabolic rate organs with greater FFM. The present study thus provides a new approach for examining REE-FFM relationships, advances in a quantitative manner previously observed albeit incompletely formulated REE-body composition associations, and identifies areas in need of additional research.     

Resistance training increases total energy expenditure and free-living physical activity in older adults.

The purpose of this study was to determine what effects 26 wk of resistance training have on resting energy expenditure (REE), total free-living energy expenditure (TEE), activity-related energy expenditure (AEE), engagement in free-living physical activity as measured by the activity-related time equivalent (ARTE) index, and respiratory exchange ratio (RER) in 61- to 77-yr-old men (n = 8) and women (n = 7). Before and after training, body composition (four-compartment model), strength, REE, TEE (doubly labeled water), AEE (TEE - REE + thermic response to meals), and ARTE (AEE adjusted for energy cost of standard activities) were evaluated. Strength (36%) and fat-free mass (2 kg) significantly increased, but body weight did not change. REE increased 6.8%, whereas resting RER decreased from 0.86 to 0.83. TEE (12%) and ARTE (38%) increased significantly, and AEE (30%) approached significance (P = 0.06). The TEE increase remained significant even after adjustment for the energy expenditure of the resistance training. In response to resistance training, TEE increased and RER decreased. The increase in TEE occurred as a result of increases in both REE and physical activity. These results suggest that resistance training may have value in increasing energy expenditure and lipid oxidation rates in older adults, thereby improving their metabolic profiles.     

Genetic Pleiotropy for Resting Metabolic Rate with Fat-Free Mass and Fat Mass: The Québec Family Study

Shared genetic and familial environmental causes for the associations among resting metabolic rate (RMR), fat-free mass (FFM), and fat mass (FM) were investigated in families participating in phase 2 of the Québec Family Study. A multivariate familial correlation model assessing the pattern of significant cross-trait correlations between family members (e.g., RMR in parents with FFM in offspring) was used to infer the etiology of the associations. For each of FM and FFM with RMR, significant sibling, parent-offspring, and intraindividual cross-trait correlations suggest the associations are familial. Furthermore, the lack of significant spouse cross-trait correlations suggests that the familial aggregation is primarily genetic. Bivariate heritability estimates suggest that as much as 45% to 50% of the shared variance between FFM and RMR may be genetic, and as much as 28% to 34% for FM and RMR. This study supports the notion that the gene(s) affecting each of FFM and FM also influence the RMR. Moreover, the lack of any familial associations between FFM and FM suggests that the effects of each body size component on RMR are independent, i.e., more than one genetic source on the RMR-body size association. The possibility that RMR is an oligogenic trait (i.e., more than one underlying genetic etiology) should be further investigated using more complex multivariate segregation methods until specific genes can be tested.     

Assessment of physical activity in older individuals: a doubly labeled water study.

We compared the accuracy of two physical activity recall questionnaires and a motion detector in 45- to 84-yr-old women (n = 35) and men (n = 32), using doubly labeled water (DLW) in conjunction with indirect calorimetry as the criterion measure. Subjects were administered the Yale Physical Activity Survey (YPAS) and Minnesota Leisure Time Physical Activity Questionnaire (LTA). Physical activity energy expenditure was determined over a 10-day period by using a Caltrac uniaxial accelerometer and DLW in conjunction with indirect calorimetry. In older women, Minnesota LTA (386 +/- 228 kcal/day) and Caltrac (379 +/- 162 kcal/day) underestimated physical activity by approximately 55% compared with DLW (873 +/- 244 kcal/day). No difference was observed between daily physical activity measured by the YPAS (863 +/- 447 kcal/day) and DLW in older women. In older men, Minnesota LTA (459 +/- 288 kcal/day) and Caltrac (554 +/- 242 kcal/day) underestimated daily physical activity by approximately 50-60% compared with DLW (1,211 +/- 429 kcal/day). No difference was found between physical activity measured by the YPAS (1,107 +/- 612 kcal/day) and DLW in older men. Despite no difference in mean physical activity levels between YPAS and DLW in women and men, Bland and Altman (Lancet 1: 307-310, 1986) analyses demonstrated poor concordance between DLW and YPAS (i.e., limits of agreement = -1,310-1,518 kcal/day). Our data suggest that the Minnesota LTA recall and Caltrac uniaxial accelerometer may significantly underestimate free-living daily physical activity energy expenditure in older women and men. Although the YPAS compares favorably with DLW on a group basis, its use as a proxy measure of individual daily physical activity energy expenditure may be limited in older women and men.