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Estimation de la dépense énergétique chez des personnes diabétiques de type 2 à l'aide de capteurs "intelligents"

Abstract : The aim of this thesis is to review the methods and tools validated in T2D patients and to developed precise and adapted EE estimation method for these patients. After defining the most common daily activity in T2D patients (Study 1), we are interested, in particular, on static activities and walking. The results of our second study showed that the gross and net metabolic rate were 8.7 % and 14.3 % higher in T2D patients than in control subjects, respectively. These results highlight the need to validate an EE estimation method, adapted to diabetic’s specificities. The results of our third study demonstrated the validity of the activity-related EE estimated with a smart sensor and a validated algorithm during static activities and walking in T2D patients and healthy subjects. However, our results showed a lower accuracy of the estimated EE during walking in T2D patients in comparison with healthy subjects. Finally, in a fourth study, three equations estimating the total EE have been developed with acceleration data from three smart sensors (lower back, hip and ankle) and personal data (body mass index, sex, diabetes) from T2D and healthy subjects. These three equations allow to estimate the total EE during static activities and walking with a mean bias of 0.009, -0.007 et -0.008 kcal/min for the lower back, hip and ankle equation, respectively, in comparison with the reference value. Thus, this thesis enables to validate three suitable equations for the estimation of total EE in population including healthy and T2D patients, normal-weighted and overweighed and aged between 35 and 80 years, based on data from a smart sensor.
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Submitted on : Tuesday, October 22, 2019 - 2:04:26 PM
Last modification on : Monday, April 6, 2020 - 2:49:31 PM
Long-term archiving on: : Tuesday, September 25, 2018 - 6:25:23 AM


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  • HAL Id : tel-01820631, version 1


Nathan Caron. Estimation de la dépense énergétique chez des personnes diabétiques de type 2 à l'aide de capteurs "intelligents". Santé. Université de la Réunion, 2017. Français. ⟨NNT : 2017LARE0048⟩. ⟨tel-01820631⟩



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