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Sökning: WFRF:(Bonomi Alberto G.)

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1.
  • Speakman, John R., et al. (författare)
  • Total daily energy expenditure has declined over the past three decades due to declining basal expenditure, not reduced activity expenditure
  • 2023
  • Ingår i: Nature Metabolism. - : NATURE PORTFOLIO. - 2522-5812. ; 5:4, s. 579-588
  • Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
    • Obesity is caused by a prolonged positive energy balance(1,2). Whether reduced energy expenditure stemming from reduced activity levels contributes is debated(3,4). Here we show that in both sexes, total energy expenditure (TEE) adjusted for body composition and age declined since the late 1980s, while adjusted activity energy expenditure increased over time. We use the International Atomic Energy Agency Doubly Labelled Water database on energy expenditure of adults in the United States and Europe (n = 4,799) to explore patterns in total (TEE: n = 4,799), basal (BEE: n = 1,432) and physical activity energy expenditure (n = 1,432) over time. In males, adjusted BEE decreased significantly, but in females this did not reach significance. A larger dataset of basal metabolic rate (equivalent to BEE) measurements of 9,912 adults across 163 studies spanning 100 years replicates the decline in BEE in both sexes. We conclude that increasing obesity in the United States/Europe has probably not been fuelled by reduced physical activity leading to lowered TEE. We identify here a decline in adjusted BEE as a previously unrecognized factor.
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2.
  • Gyllensten, Illapha Cuba, et al. (författare)
  • Identifying Types of Physical Activity With a Single Accelerometer : Evaluating Laboratory-trained Algorithms in Daily Life
  • 2011
  • Ingår i: IEEE Transactions on Biomedical Engineering. - 0018-9294 .- 1558-2531. ; 58:9, s. 2656-2663
  • Tidskriftsartikel (refereegranskat)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 behavior. This study is aimed at analyzing 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 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.
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