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Both IPAQ and SP2PAQ underestimated average energy expenditure from moderate activity, but overestimated average energy expenditure from vigorous activity as compared with the accelerometer.
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It should be noted, that grain-size distribution diagram of Nano PEG-300 (Fig. 3a) shows deviation from the symmetrical Gauss distribution which can cause an overestimated number of average grain size of this sample.
Compared to the GPS data, the self-reported data on average overestimated exposure by 6.1% and misclassified exposure by -21.6 to 40.3% (mean: 6.6%; standard deviation: 0.13) for individual in this subset.
Compared to the GPS data, the self-reported data on average overestimated exposure by 15.8% for the study population and misclassified exposure by -44.0 to 308.1% (mean: 19.2%; standard deviation: 0.44) for individual subjects.
Figure 3 shows that standard deviation of the estimates is not larger than in the case of A. Consequently, the variance of D is not accurately estimated, but is estimated more accurately than the variance of A. Figure 4 shows that when three of the four factors affect a trait, the biases of the explained variance of D are overestimated (12.0% on average).
Simulated soil temperatures were overestimated (underestimated) during the summer winterr) on average by 2 5 °C.
In contrast, FBA systematically overestimated growth rates by 0.48 on average (p-value: 7.5·10-9).
On average, the 7D-PAR overestimated TEE and AEE for our sample of older adults.
They found, on average, there was an overestimated exposure of 15.8% using the self-reported diaries.
Based on our findings, 4D-MSPECT overestimated these values by 4% on average in poststress and rest acquisitions.
However, none of them obtained measurements, which were above or below the average, but they have, at times, overestimated or underestimated certain measurements when using both techniques.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com