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First published online January 30, 2009
Journal of Experimental Biology 212, 471-482 (2009)
Published by The Company of Biologists 2009
doi: 10.1242/jeb.026377

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Estimating energy expenditure of animals using the accelerometry technique: activity, inactivity and comparison with the heart-rate technique

J. A. Green^1,*, L. G. Halsey², R. P. Wilson³ and P. B. Frappell^4,

¹ School of Biological Sciences, University of Liverpool, Crown Street, Liverpool, L69 7ZB, UK
² School of Human and Life Sciences, Roehampton University, Holybourne Avenue, London, SW15 4JD, UK
³ Institute of Environmental Sustainability, School of the Environment and Society, University of Swansea, Singleton Park, Swansea, SA2 8PP, UK
⁴ Department of Zoology, La Trobe University, Bundoora, Melbourne, Victoria 3070, Australia

View larger version (4K): [in this window] [in a new window]   Fig. 1. Measures of acceleration recorded along the x-axis (wing to wing, red trace) and z-axis (ventral to dorsal, green trace) in a bantam chicken (at 18°C unless otherwise stated). The traces for both axes while the chicken is eating are clearly different to the traces during sleeping and during walking. However, the traces for sleeping while exhibiting an SDA and resting while at an ambient temperature below the thermoneutral zone (TNZ; 1°C) are more difficult to distinguish.

View larger version (4K): [in this window] [in a new window]   Fig. 2. Mean (±s.e.m.) values of (A) partial dynamic body acceleration in the x and z axes (PDBAxz) and (B) heart rate (white bars, all ± s.e.m.) measured in bantam chickens (N=8) while they ate a meal of food pellets or rested at the same constant temperature (18°C) as a control. Concurrently made measurements of the rate of oxygen consumption (black bars, ±s.e.m.) are also shown. Significant differences between control and eating states are indicated by the following symbols: * and #.

View larger version (5K): [in this window] [in a new window]   Fig. 3. Mean (±s.e.m.) values of (A) partial dynamic body acceleration in the x and z axes (PDBAxz) and (B) heart rate (white bars, all ±s.e.m.) measured in bantam chickens (N=8) while they digested a meal of food pellets or rested while post-absorptive at the same constant temperature (18°C) as a control. Concurrently made measurements of the rate of oxygen consumption (black bars, ±s.e.m.) are also shown. Significant differences between control and digesting states are indicated by the following symbols: * and #.

View larger version (5K): [in this window] [in a new window]   Fig. 4. Mean (±s.e.m.) values of (A) partial dynamic body acceleration in the x and z axes (PDBAxz) and (B) heart rate (white symbols) measured in bantam chickens (N=8) at a range of ambient temperature along with concurrent measurements of mean (±s.e.m.) rate of oxygen consumption (black symbols).

View larger version (5K): [in this window] [in a new window]   Fig. 5. Mean (±s.e.m.) values of (A) partial dynamic body acceleration in the x and z axes (PDBAxz) and (B) heart rate (white symbols) measured in bantam chickens (N=8) while walking on a treadmill at different speeds along with concurrent measurements of mean (±s.e.m.) rate of oxygen consumption (black symbols). Weighted regression relationships are also shown for heart rate and PDBAxz (solid lines) and rate of oxygen consumption (broken lines).

View larger version (5K): [in this window] [in a new window]   Fig. 6. Rate of oxygen consumption as a function of (A) partial dynamic body acceleration in the x and z axes (PDBAxz) and (B) heart rate across a range of behaviours in a single bantam chicken (ID 73C5). In each case, data were recorded while the chicken walked on a treadmill (filled squares), ate a meal of food pellets (filled triangles), digested the meal of food pellets (open triangles) or thermoregulated (open squares). Data shown in (C) and (D) are the same as in (A) and (B), respectively, but with the x-axis displayed as a logarithmic scale.

View larger version (3K): [in this window] [in a new window]   Fig. 7. Rate of oxygen consumption as a function of partial dynamic body acceleration in the x and z axes (PDBAxz) in eight bantam chickens. Data were recorded while the chickens walked on a treadmill (filled squares), ate a meal of food pellets (filled triangles), digested the meal of food pellets (open triangles) or thermoregulated (open squares). Also plotted is a best-fit regression line (solid line) and 95% confidence intervals (black broken lines) and 95% prediction intervals (grey broken lines). 95% confidence intervals were calculated as if s O2 was estimated from one measurement of PDBAxz during one additional behaviour by one additional chicken. 95% prediction intervals were calculated as if s O2 was estimated from 10,000 measurements of PDBAxz of four additional behaviours by 100 additional chickens, effectively the smallest possible prediction interval for this model.

View larger version (4K): [in this window] [in a new window]   Fig. 8. Rate of oxygen consumption as a function of partial dynamic body acceleration in the x and z axes (PDBAxz) in eight bantam chickens. Data were recorded while the chickens walked on a treadmill (filled squares), ate a meal of food pellets (filled triangles), digested the meal of food pellets (open triangles) or thermoregulated (open squares). Also plotted are two best-fit regression lines (solid line) and 95% confidence intervals (black broken lines) and 95% prediction intervals (grey broken lines). 95% confidence intervals were calculated as if s O2 was estimated from one measurement of PDBAxz, during one additional behaviour by one additional chicken. 95% prediction intervals were calculated as if s O2 was estimated from 10,000 measurements of PDBAxz, of four additional behaviours by 100 additional chickens, effectively the smallest possible prediction interval for this model. The x-axis is displayed on a logarithmic scale to show the clustering of data points while the chickens were inactive (digesting or thermoregulating).

View larger version (4K): [in this window] [in a new window]   Fig. 9. Rate of oxygen consumption as a function of heart rate in eight bantam chickens. Data were recorded while the chickens walked on a treadmill (filled squares), ate a meal of food pellets (filled triangles), digested the meal of food pellets (open triangles) or thermoregulated (open squares). Also plotted are two best-fit regression lines (solid line) and 95% confidence intervals (black dashed lines) and 95% prediction intervals (grey broken lines). 95% confidence intervals were calculated as if s O2 was estimated from one measurement of heart rate, during one additional behaviour by one additional chicken. 95% prediction intervals were calculated as if s O2 was estimated from 10,000 measurements of heart rate, during four additional behaviours by 100 additional chickens, effectively the smallest possible prediction interval for this model.

View larger version (4K): [in this window] [in a new window]   Fig. 10. Rate of oxygen consumption as a function of heart rate in eight bantam chickens. Data were recorded while the chickens walked on a treadmill (filled squares), ate a meal of food pellets (filled triangles), digested the meal of food pellets (open triangles) or thermoregulated (open squares). Also plotted are best-fit regression lines (solid line) and 95% confidence intervals (black broken lines) and 95% prediction intervals (grey broken lines). 95% confidence intervals were calculated as if s O2 was estimated from one measurement of heart rate, during one behaviour by one additional chicken. 95% prediction intervals were calculated as if s O2 was estimated from 10,000 measurements of heart rate, during four additional behaviours by 100 additional chickens, effectively the smallest possible prediction interval for this model.

View larger version (5K): [in this window] [in a new window]   Fig. 11. Simulation showing the effect of model selection on precision and accuracy when estimating the rate of oxygen consumption (O2) in bantam chickens. A day in the life of a chicken was repeatedly simulated where the proportion of time spent `active' was varied between 0 and 100%. (A) O2, (B) the standard error of the estimate (s.e.e.) and (C) the coefficient of variation (CV=100*s.e.e./Estimate) were calculated for this range of activity using four predictive approaches. Each of the four approaches used either partial dynamic body acceleration in the x and z axis (PDBAxz) or heart rate (fH) to predict O2. The approaches used were (1) one-model using PDBAxz (black solid lines), (2) two-model using PDBAxz (grey solid lines), (3) one-model using fH (black broken lines), (4) two-model using fH (grey broken lines). See text for further details of the four predictive approaches.

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