1. Estimating energy expenditure from accelerometer data in healthy adults and patients with type 2 diabetes
- Author
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Nathan Caron, Georges Dalleau, Teddy Caderby, Nicolas Peyrot, Chantal Verkindt, Ingénierie, Recherche et Intervention, Sport Santé et Environnement (IRISSE), Université de La Réunion (UR), Motricité, interaction, performance EA 4334 (MIP), Université de Nantes - UFR des Sciences et Techniques des Activités Physiques et Sportives (UFR STAPS), Université de Nantes (UN)-Université de Nantes (UN)-Le Mans Université (UM), This work was supported by a Regional Research Grant (grant #D2015033168) from the Région Réunion and from the European Regional Development Fund (FEDER), Le Mans Université (UM)-Université de Nantes - UFR des Sciences et Techniques des Activités Physiques et Sportives (UFR STAPS), Université de Nantes (UN)-Université de Nantes (UN), Motricité, interactions, performance EA 4334 / Movement - Interactions - Performance (MIP), and Le Mans Université (UM)-Centre hospitalier universitaire de Nantes (CHU Nantes)-Université de Nantes - UFR des Sciences et Techniques des Activités Physiques et Sportives (UFR STAPS)
- Subjects
0301 basic medicine ,Aging ,medicine.medical_specialty ,[SDV]Life Sciences [q-bio] ,Type 2 diabetes ,Biochemistry ,Treadmill walking ,03 medical and health sciences ,Acceleration ,0302 clinical medicine ,Endocrinology ,Physical medicine and rehabilitation ,Diabetes mellitus ,Genetics ,medicine ,Accelerometer data ,Molecular Biology ,Exercise ,2. Zero hunger ,Public health ,business.industry ,Cell Biology ,Energy metabolism ,Stepwise regression ,medicine.disease ,030104 developmental biology ,medicine.anatomical_structure ,Inertial measurement unit ,Energy expenditure ,Ankle ,business ,Body mass index ,030217 neurology & neurosurgery - Abstract
International audience; ObjectiveThe aim of this study was to develop specific prediction equations based on acceleration data measured at three body sites for estimating energy expenditure (EE) during static and active conditions in middle-aged and older adults with and without type 2 diabetes (T2D).Research methodsForty patients with T2D (age: 40–74 yr, body mass index (BMI): 21–29.4 kg·m−2) and healthy participants (age: 47–79 yr, BMI: 20.2–29.8 kg·m−2) completed trials in both static conditions and treadmill walking. For all trials, gas exchange was monitored using indirect calorimetry and vector magnitude was calculated from acceleration data measured using inertial measurement units placed to the participant's center of mass (CM), hip and ankle. Stepwise multiple regression analyses were conducted to select relevant variables to include in the three EE prediction equations, and three Monte Carlo cross-validation procedures were used to evaluate each separate equation.ResultsVector magnitude (p < 0.0001) and personal data (gender, diabetes status and BMI; p < 0.0001) were used to develop three linear prediction equations to estimate EE during static conditions and walking. Cross-validation revealed similar robust coefficients of determination (R2: 0.81 to 0.85) and small bias (mean bias: 0.008 to −0.005 kcal·min−1) for all three equations. However, the equation based on CM acceleration exhibited the lowest root mean square error (0.60 kcal·min−1 vs. 0.65 and 0.69 kcal·min−1 for the hip and ankle equations, respectively; p < 0.001).ConclusionThe three equations based on acceleration data and participant characteristics accurately estimated EE during sedentary conditions and walking in middle-aged and older adults, with or without diabetes.
- Published
- 2020