PLASQUI, GUY, AND KLAAS R. WESTERTERP. Physical activity assessment with accelerometers: an evaluation against doubly labeled water. Obesity. 2007;15: 2371-2379. This review focuses on the ability of different accelerometers to assess daily physical activity as compared with the doubly labeled water (DLW) technique, which is considered the gold standard for measuring energy expenditure under free-living conditions. The PubMed Central database (U.S. NIH free digital archive of biomedical and life sciences journal literature) was searched using the following key words: doubly or double labeled or labeled water in combination with accelerometer, accelerometry, motion sensor, or activity monitor. In total, 41 articles were identified, and screening the articles' references resulted in one extra article. Of these, 28 contained sufficient and new data. Eight different accelerometers were identified: 3 uniaxial (the Lifecorder, the Caltrac, and the CSA/MTI/Actigraph), one biaxial (the Actiwatch AW16), 2 triaxial (the Tritrac-R3D and the Tracmor), one device based on two position sensors and two motion sensors (ActiReg), and the foot-ground contact pedometer. Many studies showed poor results. Only a few mentioned partial correlations for accelerometer counts or the increase in R 2 caused by the accelerometer. The correlation between the two methods was often driven by subject characteristics such as body weight. In addition, standard errors or limits of agreement were often large or not presented. The CSA/MTI/Actigraph and the Tracmor were the two most extensively validated accelerometers. The best results were found for the Tracmor; however, this accelerometer is not yet commercially available. Of those commercially available, only the CSA/MTI/Actigraph has been proven to correlate reasonably with DLW-derived energy expenditure.

objective: Physical activity (PA) begins to decline in adolescence with a concomitant increase in weight. We hypothesized that a vicious circle may arise between decreasing PA and weight gain from adolescence to early adulthood. Methods and Procedures: PA and self-perceived physical fitness assessed in adolescents (16-18 years of age) were used to predict the development of obesity (BMI ≥30 kg/m 2 ) and abdominal obesity (waist ≥88 cm in females and ≥102 cm in males) at age 25 in 4,240 twin individuals (90% of twins born in Finland, 1975Finland, -1979. Ten 25-year-old monozygotic (MZ) twin pairs who were discordant for obesity (with a 16 kg weight difference) were then carefully evaluated for current PA (using a triaxial accelerometer), total energy expenditure (TEE, assessed by means of the doubly labeled water (DLW) method), and basal metabolic rate (BMR, assessed by indirect calorimetry). Results: Physical inactivity in adolescence strongly predicted the risk for obesity (odds ratio (OR) 3.9, 95% confidence interval (CI) 1.4-10.9) and abdominal obesity (4.8, 1.9-12.0) at age 25, even after adjusting for baseline and current BMI. Poor physical fitness in adolescence also increased the risk for overall obesity (5.1, 2.0-12.7) and abdominal obesity (3.2, 1.5-6.7) in adulthood. Physical inactivity was both causative and secondary to the development of obesity discordance in the MZ pairs. TEE did not differ between the MZ co-twins. PA was lower whereas BMR was higher in the obese co-twins. Discussion: Physical inactivity in adolescence strongly and independently predicts total (and especially) abdominal obesity in young adulthood, favoring the development of a self-perpetuating vicious circle of obesity and physical inactivity. Physical activity should therefore be seriously recommended for obesity prevention in the young.

The field of application of accelerometry is diverse and ever expanding. Because by definition all physical activities lead to energy expenditure, the doubly labelled water (DLW) method as gold standard to assess total energy expenditure over longer periods of time is the method of choice to validate accelerometers in their ability to assess daily physical activities. The aim of this paper was to provide a systematic overview of all recent (2007-2011) accelerometer validation studies using DLW as the reference. The PubMed Central database was searched using the following keywords: doubly or double labelled or labeled water in combination with accelerometer, accelerometry, motion sensor, or activity monitor. Limits were set to include articles from 2007 to 2011, as earlier publications were covered in a previous review. In total, 38 articles were identified, of which 25 were selected to contain sufficient new data. Eighteen different accelerometers were validated. There was a large variability in accelerometer output and their validity to assess daily physical activity. Activity type recognition has great potential to improve the assessment of physical activity-related health outcomes. So far, there is little evidence that adding other physiological measures such as heart rate significantly improves the estimation of energy expenditure.

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Accelerometers are often used to quantify the acceleration of the body in arbitrary units (counts) to measure physical activity (PA) and to estimate energy expenditure. The present study investigated whether the identification of types of PA with one accelerometer could improve the estimation of energy expenditure compared with activity counts. Total energy expenditure (TEE) of 15 subjects was measured with the use of double-labeled water. The physical activity level (PAL) was derived by dividing TEE by sleeping metabolic rate. Simultaneously, PA was measured with one accelerometer. Accelerometer output was processed to calculate activity counts per day (AC(D)) and to determine the daily duration of six types of common activities identified with a classification tree model. A daily metabolic value (MET(D)) was calculated as mean of the MET compendium value of each activity type weighed by the daily duration. TEE was predicted by AC(D) and body weight and by AC(D) and fat-free mass, with a standard error of estimate (SEE) of 1.47 MJ/day, and 1.2 MJ/day, respectively. The replacement in these models of AC(D) with MET(D) increased the explained variation in TEE by 9%, decreasing SEE by 0.14 MJ/day and 0.18 MJ/day, respectively. The correlation between PAL and MET(D) (R(2) = 51%) was higher than that between PAL and AC(D) (R(2) = 46%). We conclude that identification of activity types combined with MET intensity values improves the assessment of energy expenditure compared with activity counts. Future studies could develop models to objectively assess activity type and intensity to further increase accuracy of the energy expenditure estimation.

It is estimated that Asia will be the home of more than 100 million people with type 2 diabetes by the year of 2025. This region combines a high proportion of the world's population with rapidly rising diabetes prevalence rates. The increase in diabetes in Asia differs from that reported in other parts of the world: it has developed in a shorter time, in a younger age group, and in people with lower body-mass index (BMI). Studies reported that for the same BMI, Asians have a higher body fat percentage, a prominent abdominal obesity, a higher intramyocellular lipid and/or a higher liver fat content compared to Caucasians. These characteristics may contribute to a higher predisposition to insulin resistance at a lesser degree of obesity than Caucasians. The differences in body composition are more pronounced depending on the region. For the same BMI, among three major ethnic groups in Asia, Asian Indians have the highest body fat, followed by Malay and Chinese. Lower insulin sensitivity is already observed in Asian Indian adolescents with a higher body fat and abdominal obesity compared to Caucasian adolescents. In general, Asian adolescents share the same feature of body composition such as higher body subcutaneous fat, lower appendicular skeletal muscle and lower gynoid fat compared to Caucasian adolescents. This unfavourable body composition may predispose to the development of insulin resistance at later age. Genetics may play a role and the interaction with environmental factors (changes in lifestyle) could increase the risk of developing the metabolic syndrome.

Total daily energy expenditure (“total expenditure”) reflects daily energy needs and is a critical variable in human health and physiology, but its trajectory over the life course is poorly studied. We analyzed a large, diverse database of total expenditure measured by the doubly labeled water method for males and females aged 8 days to 95 years. Total expenditure increased with fat-free mass in a power-law manner, with four distinct life stages. Fat-free mass–adjusted expenditure accelerates rapidly in neonates to ~50% above adult values at ~1 year; declines slowly to adult levels by ~20 years; remains stable in adulthood (20 to 60 years), even during pregnancy; then declines in older adults. These changes shed light on human development and aging and should help shape nutrition and health strategies across the life span.

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 ϫ SMR Ϫ1 , and AEE was calculated as [(0.9 ϫ TEE) Ϫ 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) ϭ 1.00 MJ/d] and 81% of the variation in AEE (SEE ϭ 0.70 MJ/d). The partial correlations for ACD were 0.73 (p Ͻ 0.001) and 0.79 (p Ͻ 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 ϭ 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 Ͻ 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.