Prior automatic posture and activity identification improves physical activity energy expenditure prediction from hip-worn triaxial accelerometry.

Accelerometry is increasingly used to quantify physical activity (PA) and related energy expenditure (EE). Linear regression models designed to derive PAEE from accelerometry-counts have shown their limits, mostly due to the lack of consideration of the nature of activities performed. Here we tested whether a model coupling an automatic activity/posture recognition (AAR) algorithm with an activity-specific count-based model, developed in 61 subjects in laboratory conditions, improved PAEE and total EE (TEE) predictions from a hip-worn triaxial-accelerometer (ActigraphGT3X+) in free-living conditions. Data from two independent subject groups of varying body mass index and age were considered: 20 subjects engaged in a 3-h urban-circuit, with activity-by-activity reference PAEE from combined heart-rate and accelerometry monitoring (Actiheart); and 56 subjects involved in a 14-day trial, with PAEE and TEE measured using the doubly-labeled water method. PAEE was estimated from accelerometry using the activity-specific model coupled to the AAR algorithm (AAR model), a simple linear model (SLM), and equations provided by the companion-software of used activity-devices (Freedson and Actiheart models). AAR-model predictions were in closer agreement with selected references than those from other count-based models, both for PAEE during the urban-circuit (RMSE = 6.19 vs 7.90 for SLM and 9.62 kJ/min for Freedson) and for EE over the 14-day trial, reaching Actiheart performances in the latter (PAEE: RMSE = 0.93 vs. 1.53 for SLM, 1.43 for Freedson, 0.91 MJ/day for Actiheart; TEE: RMSE = 1.05 vs. 1.57 for SLM, 1.70 for Freedson, 0.95 MJ/day for Actiheart). Overall, the AAR model resulted in a 43% increase of daily PAEE variance explained by accelerometry predictions. NEW & NOTEWORTHY Although triaxial accelerometry is widely used in free-living conditions to assess the impact of physical activity energy expenditure (PAEE) on health, its precision and accuracy are often debated. Here we developed and validated an activity-specific model which, coupled with an automatic activity-recognition algorithm, improved the variance explained by the predictions from accelerometry counts by 43% of daily PAEE compared with models relying on a simple relationship between accelerometry counts and EE.

Exercise training improves fat metabolism independent of total energy expenditure in sedentary overweight men, but does not restore lean metabolic phenotype.

BACKGROUND: Obesity is a dietary fat storage disease. Although exercise prevents weight gain, effects of chronic training on dietary fat oxidation remains understudied in overweight adults. OBJECTIVE: We tested whether 2 months of training at current guidelines increase dietary fat oxidation in sedentary overweight adults like in sedentary lean adults. DESIGN: Sedentary lean (n=10) and overweight (n=9) men trained on a cycle ergometer at 50% VO2peak, 1 h day-1, four times per week, for 2 months while energy balance was clamped. Metabolic fate of [d31]palmitate and [1-13C]oleate mixed in standard meals, total substrate use, total energy expenditure (TEE), activity energy expenditure (AEE) and key muscle proteins/enzymes were measured before and at the end of the intervention. RESULTS: Conversely to lean subjects, TEE and AEE did not increase in overweight participants due to a spontaneous decrease in non-training AEE. Despite this compensatory behavior, aerobic fitness, insulin sensitivity and fat oxidation were improved by exercise training. The latter was not explained by changes in dietary fat trafficking but more likely by a coordinated response at the muscle level enhancing fat uptake, acylation and oxidation (FABPpm, CD36, FATP1, ACSL1, CPT1, mtGPAT). ACSL1 fold change positively correlated with total fasting (R2=0.59, P<0.0001) and post-prandial (R2=0.49, P=0.0006) fat oxidation whereas mtGPAT fold change negatively correlated with dietary palmitate oxidation (R2=0.40, P=0.009), suggesting modified fat trafficking between oxidation and storage within the muscle. However, for most of the measured parameters the post-training values observed in overweight adults remained lower than the pre-training values observed in the lean subjects. CONCLUSION: Independent of energy balance and TEE, exercise training at current recommendations improved fitness and fat oxidation in overweight adults. However the improved metabolic phenotype of overweight adults was not as healthy as the one of their lean counterparts before the 2-month training, likely due to the spontaneous reduction in non-training AEE.

Analytical aspects of measuring (2)H/(1)H and (18)O/(16)O ratios in urine from doubly labelled water studies by high-temperature conversion elemental analyser-isotope-ratio mass spectrometry.

RATIONALE: Total Body Water (TBW) and Total Energy Expenditure (TEE) are routinely measured in free-living conditions by the (2)H2(18)O method. Isotope eliminations can be measured from spot urine samples by HTC-EA IRMS, but only after cumbersome cryogenic distillation to extract water. Distillation may, however, be replaced by charcoal treatment and filtration. This study tested (1) the effect of sample treatments (filtration versus distillation) on the isotope ratios, (2) the effect of different ways of normalization that respect or not the principle of identical treatment of the sample and references, and (3) the impact on the biological outcomes. METHODS: Two filters (PES membrane; 10 kDa) accepting volumes of urine samples (V500: 0.5 mL versus V6: 3.0 mL) were tested. In-house water standards and in-house urine standards were prepared and normalized against the international scale to calibrate the urine samples. The δ(2)H and δ(18)O values from water in the urine were measured by HTC-EA IRMS. RESULTS: Filtered urine normalized with water standards showed a bias in the δ(2)H values that was corrected when calibration was performed with urine standards. At a δ(2)H value of 1101.4‰, the accuracy increased from -11.9 to -0.2 δ‰ (V500) and from -3.8 to 0.4 δ‰ (V6). The TBW errors were greatest with V500 and water calibration (1.20%) and lowest with V6 and urine calibration (0.34%; preparation-by-calibration interaction p = 0.027). For the δ(18)O values the accuracy of enrichments and TBW were not affected whatever preparations and normalization were used. The average TEE was not affected but the variability increased from 0.6 to 2.7% versus cryogenic distillation. CONCLUSIONS: Cryogenic distillation remains the gold standard for small sample size experiments where small changes in TEE are to be detected. Filtration offers an alternative for large-scale experiments. When the body composition is derived from (2)H2O dilution, it is strongly recommended that urine standards should be used to eliminate the effect of filtration.