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

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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Branched equation modeling of simultaneous accelerometry and heart rate monitoring improves estimate of directly measured physical activity energy expenditure

Journal of Applied Physiology ◽

10.1152/japplphysiol.00703.2003 ◽

2004 ◽

Vol 96 (1) ◽

pp. 343-351 ◽

Cited By ~ 261

Author(s):

Søren Brage ◽

Niels Brage ◽

Paul W. Franks ◽

Ulf Ekelund ◽

Man-Yu Wong ◽

...

Keyword(s):

Physical Activity ◽

Heart Rate ◽

Energy Expenditure ◽

A Priori ◽

Whole Body ◽

Equation Modeling ◽

Physical Activity Energy Expenditure ◽

Improve Measurement ◽

Post Hoc ◽

Activity Energy

The combination of heart rate (HR) monitoring and movement registration may improve measurement precision of physical activity energy expenditure (PAEE). Previous attempts have used either regression methods, which do not take full advantage of synchronized data, or have not used movement data quantitatively. The objective of the study was to assess the precision of branched model estimates of PAEE by utilizing either individual calibration (IC) of HR and accelerometry or corresponding mean group calibration (GC) equations. In 12 men (20.6-25.2 kg/m2), IC and GC equations for physical activity intensity (PAI) were derived during treadmill walking and running for both HR (Polar) and hipacceleration [Computer Science and Applications (CSA)]. HR and CSA were recorded minute by minute during 22 h of whole body calorimetry and converted into PAI in four different weightings (P1-4) of the HR vs. the CSA (1-P1-4) relationships: if CSA > x, we used the P1 weighting if HR > y, otherwise P2. Similarly, if CSA ≤ x, we used P3 if HR > z, otherwise P4. PAEE was calculated for a 12.5-h nonsleeping period as the time integral of PAI. A priori, we assumed P1 = 1, P2 = P3 = 0.5, P4 = 0, x = 5 counts/min, y = walking/running transition HR, and z = flex HR. These parameters were also estimated post hoc. Means ± SD estimation errors of a priori models were -4.4 ± 29 and 3.5 ± 20% for IC and GC, respectively. Corresponding post hoc model errors were -1.5 ± 13 and 0.1 ± 9.8%, respectively. All branched models had lower errors ( P ≤ 0.035) than single-measure estimates of CSA (less than or equal to -45%) and HR (≥39%), as well as their nonbranched combination (≥25.7%). In conclusion, combining HR and CSA by branched modeling improves estimates of PAEE. IC may be less crucial with this modeling technique.

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