Tri-axial Accelerometer For Movement Registration Research Articles

Weight-Loss Induced Changes in Physical Activity and Activity Energy Expenditure in Overweight and Obese Subjects before and after Energy Restriction

Activity energy expenditure (AEE) is the component of daily energy expenditure that is mainly influenced by the amount of physical activity (PA) and by the weight of the body displaced. This study aimed at analyzing the effect of weight loss on PA and AEE. The body weight and PA of 66 overweight and obese subjects were measured at baseline and after 12 weeks of 67% energy restriction. PA was measured using a tri-axial accelerometer for movement registration (Tracmor) and quantified in activity counts. Tracmor recordings were also processed using a classification algorithm to recognize 6 common activity types engaged in during the day. A doubly-labeled water validated equation based on Tracmor output was used to estimate AEE. After weight loss, body weight decreased by 13±4%, daily activity counts augmented by 9% (95% CI: +2%, +15%), and this increase was weakly associated with the decrease in body weight (R2 = 7%; P<0.05). After weight loss subjects were significantly (P<0.05) less sedentary (–26 min/d), and increased the time spent walking (+11 min/d) and bicycling (+4 min/d). However, AEE decreased by 0.6±0.4 MJ/d after weight loss. On average, a 2-hour/day reduction of sedentary time by increasing ambulatory and generic activities was required to restore baseline levels of AEE. In conclusion, after weight loss PA increased but the related metabolic demand did not offset the reduction in AEE due to the lower body weight. Promoting physical activity according to the extent of weight loss might increase successfulness of weight maintenance.

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/m(2)). 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-r(2) = 0.55, P < 0.001), and Cnts/d (partial r(2) = 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-r(2) = 0.31, P < 0.001) and Cnts/d (partial-r(2) = 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.

Skeletal muscle fiber‐type distribution and habitual physical activity in daily life

The capacity to perform physical activity largely depends on physical fitness. Muscle fiber-type distribution (Muscle(FTD)) is associated with physical fitness and may influence the capacity to perform physical activity. The purpose of this study was to determine whether habitual physical activity in daily life (PA(DL)) and Muscle(FTD) are related. Thirty-eight healthy non-athletes (31 women, 7 men) were recruited. PA(DL) was measured twice for 14 days using a tri-axial accelerometer for movement registration (Tracmor). From Tracmor output, the proportion of time subjects were physically active at low, moderate, and high intensities was determined (%Low, %Moderate, and %High, respectively). A total activity index (PA(index)) and sub-scores on work, leisure-time and sports were obtained using the Baecke questionnaire. Muscle(FTD) was determined using immuno-fluorescence against respective myosin heavy chain isoforms. No relationship was observed between PA(DL) and Muscle(FTD). %Low, %Moderate, and %High, as well as PA(index) and its sub-scores, were not related to Muscle(FTD) either. The time spent on sports was associated with the proportion of type I and II(X) fibers (P=0.06 and P<0.01, respectively). In conclusion, Muscle(FTD) probably cannot explain why some people are more prone to engaging in physical activities than others.

Habitual physical activity in daily life correlates positively with markers for mitochondrial capacity

Physical exercise training is a powerful tool to maintain or improve mitochondrial density and function (mitochondrial capacity). This study aims to determine whether mitochondrial capacity is also associated with habitual physical activity in daily life (PADL). The capacity of classic markers for mitochondrial density, i.e., the capacity of citrate synthase (CS) and succinate dehydrogenase (SDH), as well the capacity of cytochrome c oxidase (COX) and beta-hydroxyacyl-CoA dehydrogenase (HAD), was determined in homogenized muscle biopsy samples obtained from the vastus lateralis muscle of nonexercising healthy young (age 20+/-2 yr) subjects (31 women, 7 men). PADL was measured during two periods of 14 days using a triaxial accelerometer for movement registration. CS, SDH, and COX were positively associated with PADL [P<0.05, R=0.36, 95% confidence interval (CI): 1.3.10(-4) to 2.2.10(-3); P<0.05, R=0.39, 95% CI: 1.1.10(-5) to 9.9.10(-5); and P<0.05, R=0.33, 95% CI: 7.5.10(-6) to 3.6.10(-4), respectively], and HAD tended to correlate positively with PADL (P=0.06, R=0.31, 95% CI: -2.2.10(-5) to 1.1.10(-3)). The population was subsequently stratified based on the intensity of the activities performed. CS was only associated with PADL in subjects spending more time on high-intensity physical activity, whereas HAD was only associated with PADL in subjects spending less time on low intensity physical activity. We are the first to report that even within the range of normal daily life activities, mitochondrial capacity is positively associated with the level of habitual physical activity in daily life. Thus an active lifestyle may help to maintain or improve mitochondrial capacity.

Body composition is associated with physical activity in daily life as measured using a triaxial accelerometer in both men and women

Activity-related energy expenditure is the most variable component of total energy expenditure and thus an important determinant of energy balance. To determine whether body composition is related to physical activity in both men and women. A total of 134 healthy participants were recruited (80 women, 54 men; aged 21+/-2 years; body mass index, 22.0+/-2.4). Physical activity was measured for a period of 2 weeks using a triaxial accelerometer for movement registration (Tracmor). Percentage body fat (%BF) was determined by underwater weighing and deuterium dilution according to Siri's three-compartment model. The participant characteristics-body mass, height and gender together explained a substantial part of the variation in %BF (R(2)=0.75, SEE=4.0%). Adding physical activity to the model increased the explained variation in %BF with 4% (R(2)=0.79, SEE=3.7%, P<0.001). Taking seasonality into account by adding the number of daylight hours as an independent variable further increased the explained variation with 1% (R(2)=0.80, SEE=3.7%, P<0.05). In analogy, the association was evaluated for both genders separately. In women, %BF and physical activity were significantly associated (P<0.001). In men, %BF was only associated with physical activity when seasonality was taken into account as well (P<0.05). This probably resulted from men participating more in season bound sports, because an association was found without adjusting for seasonality when only men with a consistent year-round participation in sports were considered. Evidence was found for an association between body composition and physical activity in both genders. A consistent year-round degree of physical activity appears to be a prerequisite to reveal the association. Moreover, Tracmor-assessed physical activity improves the estimate of %BF when a participant's characteristics are taken into account.

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

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. 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 x SMR(-1), and AEE was calculated as [(0.9 x TEE) - SMR]. Body composition was calculated from body weight, body volume, and total body water using Siri's three-compartment model. 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). 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.

Physical activity as measured by accelerometry in children receiving growth hormone

Aim: Parents of children treated with growth hormone (GH) frequently report to the paediatrician that their children have become more physically active. In the present study, activity patterns of GH-treated children were measured and compared to those of healthy controls. Methods: Subjects were 25 children at the start of GH treatment (age 8.4 ± 2.6 y) and 19 age- and gender-matched controls (age 8.8 ± 3.2 y). Physical activity was assessed with a tri-axial accelerometer for movement registration over two separate 2-wk intervals, one before the start of GH treatment and one 2 wk after the start of treatment. GH-treated subjects were categorized as poor responders (change in height over 1 y >0.7 SDS, n= 15) or good responders (change in height over 1 y <0.7 SDS, n= 10). Results: Before therapy, good responders showed a significantly lower physical activity compared to healthy controls, spending significantly less time on high-intensity activities. This difference disappeared 2 wk after the start of therapy. Physical activity in poor responders was not significantly different from controls before and after 2 wk of GH therapy. Conclusion: Children who respond well to GH therapy (change in height >0.7 SDS) showed a reduced amount of physical activity before therapy, which was normalized after 2 wk of GH therapy.

Physical activity as measured by accelerometry in children receiving growth hormone

Parents of children treated with growth hormone (GH) frequently report to the paediatrician that their children have become more physically active. In the present study, activity patterns of GH-treated children were measured and compared to those of healthy controls. Subjects were 25 children at the start of GH treatment (age 8.4 +/- 2.6 y) and 19 age- and gender-matched controls (age 8.8 +/- 3.2 y). Physical activity was assessed with a tri-axial accelerometer for movement registration over two separate 2-wk intervals, one before the start of GH treatment and one 2 wk after the start of treatment. GH-treated subjects were categorized as poor responders (change in height over 1 y <0.7 SDS, n = 15) or good responders (change in height over 1 y >0.7 SDS, n = 10). Before therapy, good responders showed a significantly lower physical activity compared to healthy controls, spending significantly less time on high-intensity activities. This difference disappeared 2 wk after the start of therapy. Physical activity in poor responders was not significantly different from controls before and after 2 wk of GH therapy. Children who respond well to GH therapy (change in height >0.7 SDS) showed a reduced amount of physical activity before therapy, which was normalized after 2 wk of GH therapy.

Alcohol energy intake and habitual physical activity in older adults.

Alcohol forms a significant component of many diets and it supplements rather than displaces daily energy intake. Surprisingly, alcohol intake does not systematically increase body weight. The present study assessed whether a higher level of habitual physical activity in the daily environment is associated with a higher alcohol intake. Alcohol intake as part of total food intake was measured with a 7 d dietary record while at the same time physical activity was monitored with a tri-axial accelerometer for movement registration. Subjects were twenty women and twenty-four men, aged 61+/-5 years, of BMI 27.1+/-4.6 kg/m(2). Between subjects, there was a positive association between the level of habitual physical activity and alcohol intake (r 0.41; P<0.01). The subjects with higher alcohol intake had a higher activity level. On days with and days without alcohol consumption there was no difference in physical activity within subjects. In conclusion, it was shown that subjects with higher alcohol consumption are habitually more active. This may explain the lack of increasing body weight through additional energy intake from alcohol.

Tracmor system for measuring walking energy expenditure.

Walking is an important mode of exercise and is likely to represent a major component of nonexercise activity thermogenesis. The question arises, how best to quantify walking-energy expenditure (EE) in free-living individuals. The triaxial accelerometer for movement registration (Tracmor) is a valid measure of body displacement and so we wanted to evaluate this tool for quantifying walking-EE. In this study, we test the hypothesis that walking-EE, measured in a Room Calorimeter, can be predicted from Tracmor output using a regression equation derived from a brief Tracmor/treadmill/Metabolic Cart protocol. First, 11 healthy subjects completed a 40-min procedure whereby they wore a Tracmor unit and walked on a treadmill at 0, 1, 2 and 3 mph while EE was measured using a Metabolic Cart. This allowed a regression equation to be defined for each subject to convert Tracmor output to EE. Each subject then entered a Room Calorimeter wearing the Tracmor Unit and walked at two self-selected velocities ('slow', 'fast') while EE was measured. 'Tracmor/regression equation' predictions of walking-EE were compared with Room Calorimeter measurements of walking-EE for the two velocities. The 'Tracmor/regression equation' prediction of EE for walking slowly was 6.36+/-1.67 kJ/min, and for walking fast it was 11.0+/-2.60 kJ/min. Room Calorimeter measurements were 6.43+/-1.85 and 10.9+/-3.03 kJ/min, respectively. The intraclass correlation coefficient for slow-paced walking was 0.93 (P<0.001), and for fast-paced walking it was 0.82 (P<0.005). When combined with laboratory measures of EE, the Tracmor accelerometer provides useful data on walking-EE and is applicable to free-living individuals.

Intra-individual variation of basal metabolic rate and the influence of daily habitual physical activity before testing.

The present study determined the intra-individual variation of BMR measurements, using a standard out-patient protocol, with the subjects transporting themselves to the laboratory for the BMR measurements after spending the night at home. The effect of a non-fasting state and variation in daily habitual physical activity the day before testing was evaluated. Eight male and eleven female subjects participated in three BMR measurements with 2-week intervals. Physical activity was estimated with a tri-axial accelerometer for movement registration, during the 3 d before each BMR measurement. There were no significant differences in estimated BMR (ANOVA repeated measures, P=0.88) and in physical activity (ANOVA repeated measures, P=0.21). Mean within-subject CV in BMR was found to be 3.3 (SD 2.1) %, ranging from 0.4 to 7.2 %. Differences between BMR measurements could not be explained by differences in physical activity the day before; however the mean within-subject CV in BMR changed from 5.7 to 5.2 % after correcting for within-machine variability and from 5.2 to 3.3 % after excluding five measurements because of non-compliance to the protocol including fasting. In conclusion, BMR values measured with a standard out-patient protocol are sufficiently reproducible for most practical purposes despite the within-subject variability in physical activity the day before the measurement. For this purpose, however, non-fasting subjects must be excluded and a regular function check of the ventilated-hood system is recommendable.

Energy balance in depleted ambulatory patients with chronic obstructive pulmonary disease: the effect of physical activity and oral nutritional supplementation.

Patients with chronic obstructive pulmonary disease (COPD) often suffer from weight loss. The aim of the present study was to gain insight into the energy balance of depleted ambulatory COPD patients, in relation to their habitual level of physical activity and consumption of oral nutritional supplements. Clinically stable and weight-stable patients (n 20; BMI 19.8+/- SD 2.0 kg/m2) were studied 1 and 3 months after rehabilitation or recovery in the clinic and were at random assigned to a control or intervention group with regard to nutritional supplementation. Energy intake was measured with a 7 d food record. Energy expenditure was estimated from a simultaneous 7 d assessment of physical activity with a tri-axial accelerometer for movement registration in combination with measured BMR. Body mass was measured at several time points. The body mass remained stable in both groups after 1 or 3 months and mean energy balances were comparable for both groups. The mean body-mass change between month 1 and 3 was negatively related to the mean physical activity level (r -0.49; P=0.03). Weight change over the 3 months was negatively associated with the physical activity level. These results suggest that knowledge about the individual physical activity level is necessary for the estimation of the energy need of the COPD patient.

Repeated measurement of habitual food intake increases under-reporting and induces selective under-reporting.

The aim of the current study was to measure differences in reporting behaviour between a first occasion of 7 d food recording and a second occasion of 7 d food recording 12 weeks later, in a group of elderly men (n 17) and women (n 17). Half the group followed an exercise intervention. The mean age was 61 (sd 5) years and mean BMI was 26.2 (sd 3.8) kg/m2. Reported energy intake was compared with energy expenditure as calculated from measured BMR and physical activity assessed with a tri-axial accelerometer for movement registration. Total under-reporting was divided into undereating and under-recording. Undereating was calculated from the change in body mass over the recording week and the under-recording was measured using the water balance technique. In the first period, the total under-reporting was 21 % and increased to 27% in the second period In the first period there was no indication for subjects eating less during the recording week, however, in the second period subjects lost body mass during the food recording indicating undereating. The amount of under-recording was calculated at 21% in the first period and 18% in the second period of recording (P 0.28). During the second period subjects selectively under-reported their fat intake and over-reported their protein intake. In conclusion, repeated assessment of food intake caused a higher quantitative and a qualitative under-reporting of food intake. The effect of interventions (dietary or otherwise) on habitual food intake might be confounded by changes in food-reporting behaviour.

Physical activity assessment with accelerometers.

Evaluation of motion sensors, specifically accelerometers, as an objective tool for the assessment of physical activity in large populations, over periods long enough to be representative of normal daily life and with minimal discomfort to the subjects. Review of validation studies of accelerometers with indirect calorimetry as a reference method. Accelerometers were commercially available one-axial accelerometers: Caltrac, Computer Science Application (CSA) accelerometer, Mini Motionlogger Actigraph; the tri-axial accelerometer Tritrac-R3 D; and an tri-axial accelerometer for movement registration (Tracmor) from our laboratory. There is no clear difference for correspondence between indirect calorimetry and accelerometer counts during level walking, whether one-axial or tri-axial and placed at the wrist, hip or low back. Sedentary activities are better reflected with a tri-axial accelerometer than with a one-axial accelerometer. Two accelerometers were validated in free living conditions with doubly labeled water. The highest correlation between accelerometer output and activity induced energy expenditure was found for Tracmor. From all accelerometers tested, the tri-axial accelerometer for movement registration is an objective method that can be used to distinguish differences in activity levels between individuals and assess the effect of interventions on physical activity within individuals.

Underreporting of Habitual Food Intake Is Explained by Undereating in Highly Motivated Lean Women

Underreporting of habitual food intake can be explained by underrecording and/or undereating. This study was designed to discriminate between the two errors mentioned, by measuring energy and water balance. Twenty-four lean female dieticians were recruited as subjects. Energy intake and water intake were measured for 1 wk with a weighed dietary record. Energy expenditure was estimated from measurements of resting metabolic rate, and measured physical activity with a triaxial accelerometer for movement registration. Water loss was estimated with deuterium-labeled water. Energy balance was determined by measuring the change in body mass over a nonrecording week (preceding the recording week) and over the recording week. Mean energy and water intake were 8.5 ± 1.0 MJ/d and 2.3 ± 0.5 L/d. The change in body mass in the nonrecording week was 0.1 ± 0.6 kg and in the recording week −0.6 ± 0.8 kg (paired t test; P = 0.02), indicating 16% undereating. Recorded water intake plus calculated metabolic water closely matched measured water loss (r = 0.93; P = 0.0001), which indicated a high recording precision. In conclusion, in the studied group of highly motivated lean women, there was 16% underreporting of habitual food intake, which could be explained by undereating.