Estimation of Physical Activity Energy Expenditure during Free-Living from Wrist Accelerometry in UK Adults

Background/Objective: This study investigated the validity of the Actiheart device for estimating free-living physical activity energy expenditure (PAEE) in adolescents. Subjects/Methods: Total energy expenditure (TEE) was measured in eighteen Canadian adolescents, aged 15–18 years, by DLW. Physical activity energy expenditure was calculated as 0.9 X TEE minus resting energy expenditure, assuming 10% for the thermic effect of feeding. Participants wore the chest mounted Actiheart device which records simultaneously minute-by-minute acceleration (ACC) and heart rate (HR). Using both children and adult branched equation modeling, derived from laboratory-based activity, PAEE was estimated from the ACC and HR data. Linear regression analyses examined the association between PAEE derived from the Actiheart and DLW method where DLW PAEE served as the dependent variable. Measurement of agreement between the two methods was analyzed using the Bland-Altman procedure. Results: A nonsignificant association was found between the children derived Actiheart and DLW PAEE values (R = .23, R2 = .05, p = .36); whereas a significant association was found between the adult derived Actiheart and DLW PAEE values (R = .53, R2 = .29, p < .05). Both the children and adult equation models lead to overestimations of PAEE by the Actiheart compared with the DLW method, by a mean difference of 31.42 kcal·kg−·d−1 (95% limits of agreement: −45.70 to −17.15 kcal·kg−1·d−1 and 9.80 kcal·kg−1·d−1 (95% limits of agreement: −21.22-1.72 kcal·kg−1·d−1), respectively. Conclusion: There is relatively poor measurement of agreement between the Actiheart and DLW for assessing free-living PAEE in adolescents. Future work should develop group based branched equation models specifically for adolescents to improve the utility of the device in this population.

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Energy requirements and physical activity in free-living older women and men: a doubly labeled water study

Journal of Applied Physiology ◽

10.1152/jappl.1998.85.3.1063 ◽

1998 ◽

Vol 85 (3) ◽

pp. 1063-1069 ◽

Cited By ~ 40

Author(s):

Raymond D. Starling ◽

Michael J. Toth ◽

William H. Carpenter ◽

Dwight E. Matthews ◽

Eric T. Poehlman

Keyword(s):

Physical Activity ◽

Body Composition ◽

Energy Expenditure ◽

Older Women ◽

Fat Free Mass ◽

Energy Requirements ◽

Physical Activity Energy Expenditure ◽

Doubly Labeled Water ◽

Free Living ◽

Activity Energy

Determinants of daily energy needs and physical activity are unknown in free-living elderly. This study examined determinants of daily total energy expenditure (TEE) and free-living physical activity in older women ( n = 51; age = 67 ± 6 yr) and men ( n = 48; age = 70 ± 7 yr) by using doubly labeled water and indirect calorimetry. Using multiple-regression analyses, we predicted TEE by using anthropometric, physiological, and physical activity indexes. Data were collected on resting metabolic rate (RMR), body composition, peak oxygen consumption (V˙o 2 peak), leisure time activity, and plasma thyroid hormone. Data adjusted for body composition were not different between older women and men, respectively (in kcal/day): TEE, 2,306 ± 647 vs. 2,456 ± 666; RMR, 1,463 ± 244 vs. 1,378 ± 249; and physical activity energy expenditure, 612 ± 570 vs. 832 ± 581. In a subgroup of 70 women and men, RMR andV˙o 2 peakexplained approximately two-thirds of the variance in TEE ( R 2 = 0.62; standard error of the estimate = ±348 kcal/day). Crossvalidation of this equation in the remaining 29 women and men was successful, with no difference between predicted and measured TEE (2,364 ± 398 and 2,406 ± 571 kcal/day, respectively). The strongest predictors of physical activity energy expenditure ( P < 0.05) for women and men were V˙o 2 peak( r = 0.43), fat-free mass ( r = 0.39), and body mass ( r = 0.34). In summary, RMR andV˙o 2 peak are important independent predictors of energy requirements in the elderly. Furthermore, cardiovascular fitness and fat-free mass are moderate predictors of physical activity in free-living elderly.

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Validation of Combined Heart Rate and Movement Sensing to Estimate Free-living Physical Activity Energy Expenditure

Medicine & Science in Sports & Exercise ◽

10.1249/01.mss.0000272982.69000.0a ◽

2007 ◽

Vol 39 (Supplement) ◽

pp. S26

Author(s):

Soren Brage ◽

Ulf Ekelund ◽

Paul W. Franks ◽

Mark A. Hennings ◽

Antony Wright ◽

...

Keyword(s):

Physical Activity ◽

Heart Rate ◽

Energy Expenditure ◽

Physical Activity Energy Expenditure ◽

Free Living ◽

Activity Energy

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Free-Living Physical Activity Energy Expenditure Is Strongly Related to Glucose Intolerance in Cameroonian Adults Independently of Obesity

Diabetes Care ◽

10.2337/dc08-1538 ◽

2008 ◽

Vol 32 (2) ◽

pp. 367-369 ◽

Cited By ~ 14

Author(s):

F. K. Assah ◽

U. Ekelund ◽

S. Brage ◽

J. C. Mbanya ◽

N. J. Wareham

Keyword(s):

Physical Activity ◽

Energy Expenditure ◽

Glucose Intolerance ◽

Physical Activity Energy Expenditure ◽

Free Living ◽

Activity Energy

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

Journal of Applied Physiology ◽

10.1152/japplphysiol.00556.2017 ◽

2018 ◽

Vol 124 (3) ◽

pp. 780-790 ◽

Cited By ~ 6

Author(s):

M. Garnotel ◽

T. Bastian ◽

H. M. Romero-Ugalde ◽

A. Maire ◽

J. Dugas ◽

...

Keyword(s):

Physical Activity ◽

Energy Expenditure ◽

Specific Model ◽

Living Conditions ◽

Physical Activity Energy Expenditure ◽

Free Living ◽

Automatic Activity ◽

Activity Energy ◽

Free Living Conditions ◽

Variance Explained

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.

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