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J Appl Physiol (September 12, 2003). doi:10.1152/japplphysiol.00703.2003
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Submitted on July 8, 2003
Accepted on September 8, 2003

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

Soren Brage1*, Niels Brage2, Paul W. Franks3, Ulf Ekelund4, Man-Yu Wong5, Lars Bo Andersen6, Karsten Froberg2, and Nicholas J Wareham3

1 Institute of Sport Science & Clinical Biomechanics, University of Southern Denmark, Odense, Denmark; Institute of Public Health, University of Cambridge, United Kingdom
2 Institute of Sport Science & Clinical Biomechanics, University of Southern Denmark, Odense, Denmark
3 Institute of Public Health, University of Cambridge, United Kingdom
4 Institute of Public Health, University of Cambridge, United Kingdom; Department of Physical Education & Health, Orebro University, Sweden
5 Institute of Public Health, University of Cambridge, United Kingdom; Department of Mathematics, Hong Kong University of Science & Technology, Hong Kong
6 Institute of Sport Science, University of Copenhagen, Denmark

* To whom correspondence should be addressed. E-mail: sb400{at}medschl.cam.ac.uk.

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, utilizing either individual calibration (IC) of HR and accelerometry or corresponding mean group calibration (GC) equations. In 12 males (20.6-25.2 kg.m-2), IC and GC equations for physical activity intensity (PAI) were derived during treadmill walking and running for both HR (Polar) and hip-acceleration (CSA). HR and CSA were recorded minute-by-minute during 22hrs 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 12.5hr non-sleeping period as the time-integral of PAI. A priori, we assumed P1=1, P2=P3=0.5, P4=0, X=5counts.min-1, Y=walking/running transition HR, and Z=flex HR. These parameters were also estimated post hoc. Mean±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%. All branched models had lower errors (p<=0.035) than single-measure estimates of CSA (<=-45%) and HR (>=+39%), as well as their non-branched combination (>=+25.7%). In conclusion, combining HR and CSA by branched modeling improves estimates of PAEE. Individual calibration may be less crucial with this modeling technique.




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