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.

This study demonstrated the ability of a triaxial accelerometer in detecting type, duration, and intensity of physical activity using models based on acceleration features. Future studies are needed to validate the presented models in free-living conditions.

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.

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.

Abstract-Accurate identification of physical activity types has been achieved in laboratory conditions using single-site accelerometers and classification algorithms. This methodology is then applied to free-living subjects to determine activity behaviour. This study aimed at analysing the reproducibility of the accuracy of laboratory-trained classification algorithms in free-living subjects during daily life. A support vector machine (SVM), a feed-forward neural network (NN) and a decision tree (DT) were trained with data collected by a waist-mounted accelerometer during a laboratory trial. The reproducibility of the classification performance was tested on data collected in daily life using a multiple-site accelerometer (IDEEA) augmented with an activity diary for 20 healthy subjects (age: 30 ± 9; BMI: 23.0 ± 2.6 kg/m 2 ). Leave-one-subject-out cross-validation of the training data showed accuracies of 95.1 ± 4.3%, 91.4 ± 6.7% and 92.2 ± 6.6% for the SVM, NN and DT, respectively. All algorithms showed a significantly decreased accuracy in daily life as compared to the reference truth represented by the IDEEA and diary classifications (75.6 ± 10.4%, 74.8 ± 9.7%, and 72.2 ± 10.3%; p<0.05). In conclusion, cross-validation of training data overestimates the accuracy of the classification algorithms in daily life.Index Terms-Assessment of daily physical activity, classification algorithms, IDEEA, physical activity, triaxial accelerometer

Aims/hypothesisThe present study compares the impact of endurance- vs resistance-type exercise on subsequent 24 h blood glucose homeostasis in individuals with impaired glucose tolerance (IGT) and type 2 diabetes.MethodsFifteen individuals with IGT, 15 type 2 diabetic patients treated with exogenous insulin (INS), and 15 type 2 diabetic patients treated with oral glucose-lowering medication (OGLM) participated in a randomised crossover experiment. Participants were studied on three occasions for 3 days under strict dietary standardisation, but otherwise free-living conditions. Blood glucose homeostasis was assessed by ambulatory continuous glucose monitoring over the 24 h period following a 45 min session of resistance-type exercise (75% one repetition maximum), endurance-type exercise (50% maximum workload capacity) or no exercise at all.ResultsAverage 24 h blood glucose concentrations were reduced from 7.4 ± 0.2, 9.6 ± 0.5 and 9.2 ± 0.7 mmol/l during the control experiment to 6.9 ± 0.2, 8.6 ± 0.4 and 8.1 ± 0.5 mmol/l (resistance-type exercise) and 6.8 ± 0.2, 8.6 ± 0.5 and 8.5 ± 0.5 mmol/l (endurance-type exercise) over the 24 h period following a single bout of exercise in the IGT, OGLM and INS groups, respectively (p < 0.001 for both treatments). The prevalence of hyperglycaemia (blood glucose >10 mmol/l) was reduced by 35 ± 7 and 33 ± 11% over the 24 h period following a single session of resistance- and endurance-type exercise, respectively (p < 0.001 for both treatments).Conclusions/interpretationA single session of resistance- or endurance-type exercise substantially reduces the prevalence of hyperglycaemia during the subsequent 24 h period in individuals with IGT, and in insulin-treated and non-insulin-treated type 2 diabetic patients. Both resistance- and endurance-type exercise can be integrated in exercise intervention programmes designed to improve glycaemic control.Trial registration:Clinicaltrials.gov NCT00945165Funding:The Netherlands Organization for Health Research and Development (ZonMw, the Netherlands).

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 (TracmorD) (Philips New Wellness Solutions, Lifestyle Incubator, the Netherlands). TracmorD 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 TracmorD 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.

Assessment of the functional capacity of the cardiovascular system is essential in sports medicine. For athletes, the maximal oxygen uptake [Formula: see text] provides valuable information about their aerobic power. In the clinical setting, the (VO(2max)) provides important diagnostic and prognostic information in several clinical populations, such as patients with coronary artery disease or heart failure. Likewise, VO(2max) assessment can be very important to evaluate fitness in asymptomatic adults. Although direct determination of [VO(2max) is the most accurate method, it requires a maximal level of exertion, which brings a higher risk of adverse events in individuals with an intermediate to high risk of cardiovascular problems. Estimation of VO(2max) during submaximal exercise testing can offer a precious alternative. Over the past decades, many protocols have been developed for this purpose. The present review gives an overview of these submaximal protocols and aims to facilitate appropriate test selection in sports, clinical, and home settings. Several factors must be considered when selecting a protocol: (i) The population being tested and its specific needs in terms of safety, supervision, and accuracy and repeatability of the VO(2max) estimation. (ii) The parameters upon which the prediction is based (e.g. heart rate, power output, rating of perceived exertion [RPE]), as well as the need for additional clinically relevant parameters (e.g. blood pressure, ECG). (iii) The appropriate test modality that should meet the above-mentioned requirements should also be in line with the functional mobility of the target population, and depends on the available equipment. In the sports setting, high repeatability is crucial to track training-induced seasonal changes. In the clinical setting, special attention must be paid to the test modality, because multiple physiological parameters often need to be measured during test execution. When estimating VO(2max), one has to be aware of the effects of medication on heart rate-based submaximal protocols. In the home setting, the submaximal protocols need to be accessible to users with a broad range of characteristics in terms of age, equipment, time available, and an absence of supervision. In this setting, the smart use of sensors such as accelerometers and heart rate monitors will result in protocol-free VO(2max) assessments. In conclusion, the need for a low-risk, low-cost, low-supervision, and objective evaluation of VO(2max) has brought about the development and the validation of a large number of submaximal exercise tests. It is of paramount importance to use these tests in the right context (sports, clinical, home), to consider the population in which they were developed, and to be aware of their limitations.