Validating measures of free-living physical activity in overweight and obese subjects using an accelerometer.

BACKGROUND: Free-living physical activity can be assessed with an accelerometer to estimate energy expenditure but its validity in overweight and obese subjects remains unknown. OBJECTIVE: Here, we validated published prediction equations derived in a lean population with the TracmorD accelerometer (DirectLife, Philips Consumer Lifestyle) in a population of overweight and obese. We also explored possible improvements of new equations specifically developed in overweight and obese subjects. DESIGN: Subjects were 11 men and 25 women (age: 41±7 years; body mass index: 31.0±2.5 kg m(-2)). Physical activity was monitored under free-living conditions with TracmorD, whereas total energy expenditure was measured simultaneously with doubly-labeled water. Physical activity level (PAL) and activity energy expenditure (AEE) were calculated from total energy expenditure and sleeping metabolic rate. RESULTS: The published prediction equation explained 47% of the variance of the measured PAL (P<0.001). PAL estimates were unbiased (errors (bias±95% confidence interval): -0.02±0.28). Measured and predicted AEE/body weight were highly correlated (r(2)=58%, P<0.001); however, the prediction model showed a significant bias of 8 kJ kg(-1) per day or 17.4% of the average AEE/body weight. The new prediction equation of AEE/body weight developed in the obese group showed no bias. CONCLUSIONS: In conclusion, equations derived with the TracmorD allow valid assessment of PAL and AEE/body weight in overweight and obese subjects. There is evidence that estimates of AEE/body weight could be affected by gender. Equations specifically developed in overweight and obese can improve the accuracy of predictions of AEE/body weight.

Both resistance- and endurance-type exercise reduce the prevalence of hyperglycaemia in individuals with impaired glucose tolerance and in insulin-treated and non-insulin-treated type 2 diabetic patients.

AIMS/HYPOTHESIS: The 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. METHODS: Fifteen 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. RESULTS: Average 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/INTERPRETATION: A 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 NCT00945165. FUNDING: The Netherlands Organization for Health Research and Development (ZonMw, the Netherlands).

Advances in physical activity monitoring and lifestyle interventions in obesity: a review.

Obesity represents a strong risk factor for developing chronic diseases. Strategies for disease prevention often promote lifestyle changes encouraging participation in physical activity. However, determining what amount of physical activity is necessary for achieving specific health benefits has been hampered by the lack of accurate instruments for monitoring physical activity and the related physiological outcomes. This review aims at presenting recent advances in activity-monitoring technology and their application to support interventions for health promotion. Activity monitors have evolved from step counters and measuring devices of physical activity duration and intensity to more advanced systems providing quantitative and qualitative information on the individuals' activity behavior. Correspondingly, methods to predict activity-related energy expenditure using bodily acceleration and subjects characteristics have advanced from linear regression to innovative algorithms capable of determining physical activity types and the related metabolic costs. These novel techniques can monitor modes of sedentary behavior as well as the engagement in specific activity types that helps to evaluate the effectiveness of lifestyle interventions. In conclusion, advances in activity monitoring have the potential to support the design of response-dependent physical activity recommendations that are needed to generate effective and personalized lifestyle interventions for health promotion.

Aspects of activity behavior as a determinant of the physical activity level.

This study investigated which aspects of the individuals' activity behavior determine the physical activity level (PAL). Habitual physical activity of 20 Dutch adults (age: 26-60 years, body mass index: 24.5 ± 2.7 kg/m(2)) was measured using a tri-axial accelerometer. Accelerometer output was used to identify the engagement in different types of daily activities with a classification tree algorithm. Activity behavior was described by the daily duration of sleeping, sedentary behavior (lying, sitting, and standing), walking, running, bicycling, and generic standing activities. Simultaneously, the total energy expenditure (TEE) was measured using doubly labeled water. PAL was calculated as TEE divided by sleeping metabolic rate. PAL was significantly associated (P<0.05) with sedentary time (R=-0.72), and the duration of walking (R=0.49), bicycling (R=0.77), and active standing (R=0.62). A negative association was observed between sedentary time and the duration of active standing (R=-0.87; P<0.001). A multiple-linear regression analysis showed that 75% of the variance in PAL could be predicted by the duration of bicycling (Partial R(2) =59%; P<0.01), walking (Partial R(2) =9%; P<0.05) and being sedentary (Partial R(2) =7%; P<0.05). In conclusion, there is objective evidence that sedentary time and activities related to transportation and commuting, such as walking and bicycling, contribute significantly to the average PAL.

Improving assessment of daily energy expenditure by identifying types of physical activity with a single accelerometer.

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.

Daily physical activity assessment with accelerometers: new insights and validation studies.

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.