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Nutr-484-Exercise phys > Lab Test 2 (lab 6) > Flashcards

Lab Test 2 (lab 6) Flashcards

1
Q

Anthropometric measurments are referred to as this for estimating body comp

A

field techniques

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2
Q

3 advantages of anthropometric measurements

A

inexpensive
mobile
time efficient

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3
Q

two types of measuring tools

A

fat-o-meter

lang calipers

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4
Q

disadvantage of AM

A

not as accurate as UWW

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5
Q

three AM we take to determine body comp

A

circumferences
diameters
skinfolds

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6
Q

T/F: measure circumferences in mm

A

F, cm

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7
Q

Circumferences reflect these two things

A

FW

FFW

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8
Q

T/F: there is no gender dependency with circumferences measurements

A

F, waist vs hip measurements may be slightly different

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9
Q

T/F: their is some slight site dependency with circumferences

A

T, the thigh may be more representative of FFW, where the abdomen may be more FW

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10
Q

diameters are associated with this

A

bone

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11
Q

T/F: measure diameters in cm

A

T

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12
Q

diameter measurements are the distance between these

A

two bony landmarks

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13
Q

biacromial, biailiac and bitrochantaric are all examples of

A

measurements between two bony landmarks

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14
Q

T/F: diamters reflect only FW

A

F, reflects only bony/skeletal size characteristics

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15
Q

T/F: skinfolds are measured in cm

A

F, mm

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16
Q

a skinfold measurement contains these 3 things

A

skin
subcutaneous fat
fascia (sometimes)

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17
Q

skinfold measurements reflect only this

A

FW

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18
Q

Statistical methods that are developed and used to relate 2 or more variables

A

regression equations

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19
Q

regression equation in the form of a straight line is

A

y = m(x) + b

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20
Q

example of a regression equation

A

brozeks equation

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21
Q

in the brozeks equation y =

A

% fat

22
Q

in the brozeks equation m =

A

slope or 4.57 (rise / run)

23
Q

in the brozeks equation x =

A

inverse body density

24
Q

in the brozeks equation b =

A

y-intercept or -4.142

25
Q

what to variables are we relating in the brozeks equation

A

% fat and body density

26
Q

Development of regression equations: define this

A

population, who are we measuring

27
Q

Development of regression equations: determine this with UWW

A

actual body comp using UWW

28
Q

Development of regression equations: take these

A

series of anthropometric measurments (height, weight, age, various skinfolds)

29
Q

Development of regression equations: determine the most efficient these

A

measurements based on theory

30
Q

Development of regression equations: questions to ask when developing a theory

A

where does this population carry most fat?

Is fat depositon related to factors like age, and height?

31
Q

Development of regression equations: A good AM test is this, in that you don’t want to spend to muc htime taking unnecessary measurements

A

practical

32
Q

Development of regression equations: You should choose the most efficient measures with the highest this

A

correlation

33
Q

See graphs on page 4 and 7 of lab 6 notes for graphs

A

okay

34
Q

T/F: multiple variables rarely predict better than a single variable

A

F, often times they do predict better

35
Q

Y = constant + coefficient (Xsub1) + coefficient (Xsub2) is an example of

A

multiple linear regression model

36
Q

these are extremely population specific

A

linear regression equations

37
Q

This is an example of a more generalized equation

A

quadratic (curvilinear) equation

38
Q

quadratic equations allow for this

A

less error at the extremes of the population measured

39
Q

This type of group requires a quadratic equation

A

heterogeneous population

40
Q

Criteria for choosing an equation: R-value should be greater than or equal to this for quadratic equations

A

0.90

41
Q

Criteria for choosing an equation: Standard error estimate is expressed as this

A

the units of the variable we are predicting

42
Q

Criteria for choosing an equation: standard error estimate represents this

A

error in our new regresssion equation

43
Q

Criteria for choosing an equation: standard error estimate defines the error of the prediction equation by relating your predicted values to this

A

UWW values

44
Q

how to use SEE

A

body density + or - standard error estimate

new body density is plugged into equation to find top and bottom range of %fat

45
Q

Sources of error in UWW (4)

A

exercising prior to UWW
Reading the scale wrong
Subjects level of comfort in/under water
menstruation has an effect (body water retention)

46
Q

Sources of error using SF

A

inappropriate landmarks for anthropometric measures (landmarks used for measure may or may not be representative)

47
Q

How to remove some error in SF reading

A

sites need to be accurate and reliable

sites need to be measured multible times on the same individual

48
Q

These errors can affect both SF and UWW

A

technique errors

49
Q

examples of technique errors

A

intra-tester error

inter-tester error

50
Q

intra-tester error

A

error within a tester (cannot take reliable, consistant measurements)

51
Q

inter-tester error

A

error between testers (two testers produces different results)

Nutr-484-Exercise phys (14 decks)

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