Results Despite heterogeneity in EE response to steadystate standing, no differences were found in the amount or pattern of spontaneous weight-shifting between the two phenotypes. Whilst experimentally induced weight-shifting resulted in a mean EE increase of only 11% (range: 0-25%), intermittent leg/body displacement increased EE to >1.5 METs in all participants. Conclusions Although the variability in spontaneous weight-shifting signatures between individuals does not appear to underlie heterogeneity in the energy cost of standing posture maintenance, these studies underscore the fact that leg/body displacement, rather than standing posture alone, is needed to increase EE above the currently defined sedentary threshold. Keywords AbstractPurpose Due to sedentarity-associated disease risks, there is much interest in methods to increase low-intensity physical activity. In this context, it is widely assumed that altering posture allocation can modify energy expenditure (EE) to impact body-weight regulation and health. However, we have recently shown the existence of two distinct phenotypes pertaining to the energy cost of standing-with most individuals having no sustained increase in EE during steady-state standing relative to sitting comfortably. Here, we investigated whether these distinct phenotypes are related to the presence/absence of spontaneous "weightshifting", i.e. the redistribution of body-weight from one foot to the other. Methods Using indirect calorimetry to measure EE in young adults during sitting and 10 min of steady-state standing, we examined: (i) heterogeneity in EE during standing (n = 36); (ii) EE and spontaneous weight-shifting patterns (n = 18); (iii) EE during spontaneous weight-shifting versus experimentally induced weight-shifting (n = 7), and; (iv) EE during spontaneous weight-shifting versus intermittent leg/body displacement (n = 6).

We propose a fuzzy confidence interval estimation based on the likelihood ratio. This ratio, often used in hypotheses testing seems to be an efficient tool for calculating confidence intervals since it is known to be general, and thus can be applied on any parameter. The strength of the defended procedure is to use a wide range of estimators with any type of distribution for the estimation of confidence intervals when fuzziness occurs. The theoretical approach and the detailed steps of the calculation are given. This approach is illustrated by a classical problem: a fuzzy confidence interval for the fuzzy mean in the context of a normal distribution. Finally, a comparison between the interval by the defended approach and one calculated by a frequently used expression is made. Our results show that the support set of the fuzzy interval by the defended method is smaller than the one by the known expression.

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