Lab Test 2 (lab 6) Flashcards
Anthropometric measurments are referred to as this for estimating body comp
field techniques
3 advantages of anthropometric measurements
inexpensive
mobile
time efficient
two types of measuring tools
fat-o-meter
lang calipers
disadvantage of AM
not as accurate as UWW
three AM we take to determine body comp
circumferences
diameters
skinfolds
T/F: measure circumferences in mm
F, cm
Circumferences reflect these two things
FW
FFW
T/F: there is no gender dependency with circumferences measurements
F, waist vs hip measurements may be slightly different
T/F: their is some slight site dependency with circumferences
T, the thigh may be more representative of FFW, where the abdomen may be more FW
diameters are associated with this
bone
T/F: measure diameters in cm
T
diameter measurements are the distance between these
two bony landmarks
biacromial, biailiac and bitrochantaric are all examples of
measurements between two bony landmarks
T/F: diamters reflect only FW
F, reflects only bony/skeletal size characteristics
T/F: skinfolds are measured in cm
F, mm
a skinfold measurement contains these 3 things
skin
subcutaneous fat
fascia (sometimes)
skinfold measurements reflect only this
FW
Statistical methods that are developed and used to relate 2 or more variables
regression equations
regression equation in the form of a straight line is
y = m(x) + b
example of a regression equation
brozeks equation
in the brozeks equation y =
% fat
in the brozeks equation m =
slope or 4.57 (rise / run)
in the brozeks equation x =
inverse body density
in the brozeks equation b =
y-intercept or -4.142
what to variables are we relating in the brozeks equation
% fat and body density
Development of regression equations: define this
population, who are we measuring
Development of regression equations: determine this with UWW
actual body comp using UWW
Development of regression equations: take these
series of anthropometric measurments (height, weight, age, various skinfolds)
Development of regression equations: determine the most efficient these
measurements based on theory
Development of regression equations: questions to ask when developing a theory
where does this population carry most fat?
Is fat depositon related to factors like age, and height?
Development of regression equations: A good AM test is this, in that you don’t want to spend to muc htime taking unnecessary measurements
practical
Development of regression equations: You should choose the most efficient measures with the highest this
correlation
See graphs on page 4 and 7 of lab 6 notes for graphs
okay
T/F: multiple variables rarely predict better than a single variable
F, often times they do predict better
Y = constant + coefficient (Xsub1) + coefficient (Xsub2) is an example of
multiple linear regression model
these are extremely population specific
linear regression equations
This is an example of a more generalized equation
quadratic (curvilinear) equation
quadratic equations allow for this
less error at the extremes of the population measured
This type of group requires a quadratic equation
heterogeneous population
Criteria for choosing an equation: R-value should be greater than or equal to this for quadratic equations
0.90
Criteria for choosing an equation: Standard error estimate is expressed as this
the units of the variable we are predicting
Criteria for choosing an equation: standard error estimate represents this
error in our new regresssion equation
Criteria for choosing an equation: standard error estimate defines the error of the prediction equation by relating your predicted values to this
UWW values
how to use SEE
body density + or - standard error estimate
new body density is plugged into equation to find top and bottom range of %fat
Sources of error in UWW (4)
exercising prior to UWW
Reading the scale wrong
Subjects level of comfort in/under water
menstruation has an effect (body water retention)
Sources of error using SF
inappropriate landmarks for anthropometric measures (landmarks used for measure may or may not be representative)
How to remove some error in SF reading
sites need to be accurate and reliable
sites need to be measured multible times on the same individual
These errors can affect both SF and UWW
technique errors
examples of technique errors
intra-tester error
inter-tester error
intra-tester error
error within a tester (cannot take reliable, consistant measurements)
inter-tester error
error between testers (two testers produces different results)
