Assessment of Nutrition, Physical Activity, and Anthropometric Measuring Techniques

This review evaluates the method used to assess nutrients intake and physical activity measures for modern and middle-aged youth with the PA guidelines of Australia. The study includes an evaluation of dietary habits, nutrients intake, physical activity measures, and anthropometric measures. The results show variations in nutrient intake and highlight the need for further research to determine the fitness of females' diets.

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HSN719 – Assessment Methods for Nutrition and Physical Activity Research 2018
Word Count: 2989
Assignment title
Assessment of Nutrition, Physical Activity, and Anthropometric Measuring Techniques
- A comparative study with Australian Population Norms and Recommendations

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Abstract
Purpose: The primary aim of this review was to evaluate the method used (3DD) to assess
the nutrients intake and physical activity measures for modern and middle-aged youth with
the PA guidelines of Australia. Methods: Throughout the study, healthy female students of
the third year enrolled in HSN305 course took place. 173 students of 18 years and over were
selected. A set of parallel messages were collected using 3-day daily food records. FFQ was
developed to evaluate the usual dietary habits as well as nutrients. 3-Day Physical Activity
Diary was used to measure Physical activity of the participants. From the Schofield equation,
basic metabolic rate was calculated. Results: Energy intake from fat was comparatively
higher than the other two nutrients. 95% of participants were in absorbing nutrients from fat
oriented food. 91% of participants were found to abide by the recommended percentage
intake of nutrients from protein oriented food. The comparative analysis yielded that calcium
and iron intakes by the participants were well below the EAR cut points. It reflects a
significant difference between the calcium intake measured by FFQ and 3DFR methods.
Energy expenditure measured in AAS scale was significantly higher than the measure by
3DD. The correlation by Pearson for 156 female participants reflected an almost zero
Correlation between them. There was a statistically significant difference in fat measured by
the two methods, and percentage fat measured by skinfolds was significantly higher than that
of the measures of the BIA method. Conclusion: The interpretation of the suitability of the
feed ration is complex and varies according to the average level, depending on the percentage
of participants in the relevant PA-directives or the statistical probability of insufficient
evaluation. There is a need to explore further ways to determine the fitness of females’ diets.

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Table of Contents
Abstract......................................................................................................................................2
Introduction................................................................................................................................3
Research Objective/ Aim........................................................................................................5
Participants and Methods...........................................................................................................6
Subjects...................................................................................................................................6
Measurement Instruments.......................................................................................................6
Nutritional Consumption Measures.....................................................................................6
Physical Activity Measures.................................................................................................6
Anthropometric Measures...................................................................................................7
Statistical Methods..............................................................................................................7
Results........................................................................................................................................8
Dietary intake..........................................................................................................................8
Physical Activity...................................................................................................................12
Anthropometric Characteristics............................................................................................13
Discussion................................................................................................................................16
Findings....................................................................................................................................16
Dietary intake....................................................................................................................16
Physical activity.................................................................................................................16
Anthropometric characteristics..........................................................................................16
Limitations............................................................................................................................17
Conclusion................................................................................................................................17
Future Research....................................................................................................................17
References................................................................................................................................18
Appendices...............................................................................................................................20
Practical Data...........................................................................................................................22

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Introduction
Women's nutrition exams often report inappropriate or inadequate diets compared to men
courses or nutritional intake (10). It was uniform in different races and countries. Many
studies have often compared food intake with recommendations for energy, gender, and
nutritional consumption for the general population (3, 13). The study included an indicative
average food frequency questionnaire (FFQ) and average 3DD consumption (3-day Dietary
intake) indicating the consumption needed for the needs of almost all healthy people. The use
of FFQ can overestimate 3DD, and the Estimated Average Requirement (EAR) overestimates
the nutritional needs of individuals. An accurate assessment of the physical activity of young
people was essential to quantify physical behavior and to assess the impact of interventions
on physical activity (14). The evaluation focused on 18-30 years of participants in the
methodology for evaluating youth activities.
The scientific report of the Physical Activity (PA) Advisory Committee in 2018 shows a
large amount of exercise was best for the health. The report provides a detailed overview of
the benefits of disease prevention and health promotion for the most active in the United
States from the latest scientific evidence. It builds and promotes the scientific documentation
presented in the first report in 2008 exercise guidelines. This result refers to physically active
individuals sleeping better, feeling better and working better (8, 16). There was substantial
evidence that moderate to severe physical activity can improve the quality of sleep. Over
time, individual sporting events have improved leadership performance (12).
Several studies have examined the link between the Body Mass Index (BMI), waist
circumference (WC) and skinfold measurement and energy intake, and studies have shown
that the critical nutrient components in the diet were an essential factor in the diet role of
proteins, carbohydrates, and fats in adult obesity (9, 17). A 2000 World Health Organization
report classified that more than 1.5 billion adults worldwide were obese, and about 4 million
of whom were clinically obese. According to the 2014-15 BMI measurement, nearly two-
thirds (63%) of 18-year-old Australians were overweight or obese (11).

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Source: ABS 2015
Nutritional well-being was an essential part of the health, independence, and quality of life of
the elderly. In previous studies, malnutrition was linked only to protein-energy malnutrition
and could lead to adverse health risks, including loss of independence, more extended
hospital stays, reduced functioning and low quality of life, increasing the risk of fractures and
deaths, and delaying wound healing and slow recovery surgery (2, 18). While young adults
live independently, reduced mobility, changes in appetite and economic constraints are often
linked to the manifestation of chronic diseases and their malnutrition status (12).
Research Objective/ Aim
The objective of this review was to evaluate the method used (3DD) to assess the nutrients
intake and physical activity measures for modern and middle-aged youth with the PA
guidelines of Australia. 3DD and FQQ measured nutrients intakes. Calcium micronutrient
intakes estimated by the two scales were hypothesized to be significantly different. The study
included average energy measured by AAS and 3DD, which were theorized to be statistically
independent. BMI and waist circumferences were compared with the WHO BMI
classification, and WC cut off values. Body fat percentages measured by skinfolds and BIA
were assumed to be related and possess significant positive relationship.

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Participants and Methods
Subjects
Throughout the study, healthy female students of the third year enrolled in HSN305 course
took place. The scholar selected 173 students of 18 years and over. The average age of
participants was 23.7 years (SD 5.3), aged between 19 and 56 years. To compare data,
standards, and recommendations "18 years or older "or "18-30 " year categories were used in
the query class.
Measurement Instruments
Nutritional Consumption Measures
The female partakers completed the FFQ, developed by CSIRO (Commonwealth Scientific
and Industrial Research Organization, South Australia (Adelaide)). Various study samples of
the population of Australia established the validity of the questionnaire. A set of parallel
messages were collected using 3-day food records. FFQ was developed to evaluate the usual
dietary habits as well as nutrients that promote bone health and antioxidants. It has been
tested using a 3-day meal record (3-DFR) in all female participants. Repeatability was
evaluated by comparing FFQ with 3DFR data. Nutritional information about sports nutrition
has been combined using information about the manufacturer's product, and added to the
FFQ database. The present study contains food supplements and other pathogenic substances
in edible form.
Physical Activity Measures
3-Day Physical Activity Diary was used to measure Physical activity of the participants (4).
This questionnaire was the most commonly used method for evaluating PA and was based on
the possibility that the participants can remember and recall (5). The survey varies depending
on the content of the measurement, the way the data was collected and the quality of the data,
such as the measurement intensity, differentiate the usual activities, and merely recent events,
as well as access to data. Each operation used the metabolic equivalent (ME) of the task (6,
11). The metabolic equivalent was defined as the amount of oxygen consumed at rest,
corresponding to 3.5 ml of O2 x minutes x per kilogram of body weight. The term with was a

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simple, convenient and easy-to-understand process that reflects the cost of the energy of
physical activity as a multiple of metabolism. The value of energy conductivity can be
determined by dividing the relative cost of oxygen (ml O2/kg/min) x 3.5. The AAS (Active
Australia Survey) was utilized to assess physical activity (5). The Australian Activity study
aims to measure participation in recreational sports activities and to evaluate the notion of
local health information, i.e., health benefits, of physical activity. It offers a short and reliable
range of questions that can be achieved by using an automated telephone interview (DE) or
face to face.
Anthropometric Measures
From the Schofield equation, BMR (Basic Metabolic Rate) was calculated (15). The
Schofield equation was a method of estimating the Basic Metabolic Rate (MNR) of adult
males and females. Weight was measured in kg, and standard evaluation error (SEE) was
calculated. Displaying the value means that the computed BMR can extract this much calorie.
For example, if a person was very muscular and weigh more than people who were equal in
average height and weight, he/she can add SEE to the calculated BMR. In addition to
calculating the BMR, the level of physical activity of the person or the coefficient of physical
activity (PAF) was used.
Statistical Methods
A continuous variable was represented as a standard deviation tool (Normal distribution) or
as an intermediate value with an Interquartile interval (Skewed distribution). Statistical
analysis was performed using software STATA 14 (StataCorp LP, USA). Using p < 0.05
statistical significance has been represented (T-Test or Pearson correlation). The difference
between the valuation methods obtained by different ways was evaluated using the paired t-
test. The association was established by using the Pearson correlation coefficient.
Micronutrient deficiency was calculated using the EAR cut-point method (calcium, folic acid,
vitamin C, zinc), and the process of total probability (for iron).

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Results
Dietary intake
Table 1: Descriptive Statistics for Macronutrient Intake
Nutrient IntakeStatistic (3DFR: 3 day food record) M ± SD SE Median IQR
Energy intake (kJ/d) - 3 DFR 7525.48 ± 2421.05 184.1 7257.0 2679.0
Daily % energy from fat - 3 DFR 33.50 ± 8.98 0.7 33.0 10.0
Daily % energy from carbohydrate - 3 DFR 40.06 ± 11.34 0.9 40.0 12.0
Daily % energy from protein - 3 DFR 21.24 ± 8.24 0.6 20.0 6.0
Daily % energy from alcohol - 3 DFR 1.01 ± 2.66 0.2 0.0 0.0
Table 2: Percentage of recommended limits for daily energy intake from carbohydrate, fat, and protein
The females’ macronutrient intake (carbohydrate, fat, protein) has been reported in Table 1.
Daily energy intake distribution for the group was almost normal, as the mean intake was
practically equal to median intake measure with micronutrient distribution in Table 3.
Compared with recommended guidelines intake in Table 2 of macronutrient intake, it was
observed that energy intake from fat was comparatively higher than the other two nutrients.
95% of participants were found in absorbing nutrients from fat oriented food. 91% of
participants were exposed to abide by the recommended percentage intake of nutrients from
protein oriented food. On the contrary, daily energy from carbohydrate consumption was
considerably below the percentage guidelines recommended of daily energy intake.

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Table 3: Descriptive Statistics for Micronutrient Intake by 3DFR and FQQ
Nutrient IntakeStatistic M ± SD SE Median IQR
Calcium intake (mg/d) - 3 day food record 726.94 ± 344.34 26.2 708.0 346.0
Iron intake (mg/d) - 3 day food record 11.72 ± 5.85 0.4 11.0 6.0
Folate intake (ug/d) - 3 day food record 391.24 ± 180.13 13.7 374.0 230.0
Vitamin C intake (mg/d) - 3 day food record 105.09 ± 72.14 5.5 91.0 75.0
Zinc intake (mg/d) - 3 day food record 10.92 ± 7.68 0.6 9.0 5.0
FFQ Calcium (mg/d) 630.72 ± 447.62 34.0 511.0 617.0
FFQ Dairy (serves/d) 2.10 ± 1.49 0.1 1.7 2.1
Table 3 presents the micronutrients consumption summary of the participating females from a
3-day food record. The results were also compared with the guidelines of EAR in Table 4.
The comparative analysis yielded that calcium and iron intakes by the participants were well
below the EAR cut points. This result reflected that females were consuming less iron and
calcium. The scenario of calcium intake was also recorded by the Food Frequency
Questionnaire (FFQ) for further statistical analysis. From Figure 1, consumption of iron and
calcium were found to be normally distributed. This trend eventually indicated that overall
calcium intake for females was considerably less than prescribed guidelines.
From Figure 2, the distribution of Folate and Vitamin-C intake of the participants were found
to be almost normally distributed. Table 4 indicated that scenario for Folate intake was also
pitiful since Folate intake of more than 35% participants was below the cut point. Vitamin-C
consumption pattern was comparatively better, though; Table 4 reflected that Vitamin-C
intake for some of the females in the sample was below par. Figure 2 and Figure 3 explained
that the distribution of Zinc and Vitamin-C were highly positively skewed. This result
indicated the presence of a few outlier intake data, which implied that some of the females in
the sample were high on consumption for these two micronutrients compared to other
participants.
Table 4: Percentage of class population at risk of deficiency of various nutrients

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Figure 1: Histograms for Calcium and Iron intake of the participants (Almost Normal Nature)
Figure 2: Histograms for Folate (Almost Normal Nature) and Vitamin-C (Positive Skew) intake of the
participants
Figure 3: Histograms for Zinc (Positive Skew) intake of the participants
Statistical inference was drawn to assess the difference between average calcium intake,
measured by FFQ and 3-day food record (3DFR). Estimated calcium intake for the entire
female population can be noted from the 95% CI in Table 5. The comparative analysis was
done with the help of paired t-test, and the result in Table 5 reflects a significant dissimilarity
between the calcium intake measured by FFQ and 3DFR methods. The p-value of 0.001 in t-
test indicated a statistically significant high calcium intake measured by 3-day food record
compared to FFQ scale.

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Table 5: Paired t-test between mean calcium intakes assessed via FFQ vs. 3DFR
Variable Mean ± SD Std. Err. [95% Conf. Interval]
FFQ Calcium intake (mg/d) 630.72 ± 447.62 34.0 563.5 697.9
3-day food record Calcium
intake (mg/d) 726.94 ± 344.34 26.2 675.3 778.6
Difference (-) 96.22 ± 460.30 35.0 -165.3 -27.1
N = 173, T= -2.74, degress of freedom=172, P-value=0.001
A further inferential parametric test was conducted to quantify the correlation of calcium
intake measured by 3DFR and FFQ. Figure 4 represents a symmetrical distributed and
positive relationship between the two methods. No possible outlier observations were noted
from the scatter plot in Figure 4. Pearson's correlation was used to establish and enumerate
the relationship. A mid-positive, but statistically insignificant (r = 0.41, p = 0.35)
correlation was observed between calcium intake measured by FFQ and 3DFR. Hence, the
p-value of 0.35 indicated that the presumed Correlation was not significant, and was
supported by the analysis result in Table 5.
Figure 4: Scatterplot for Comparison of Calcium intake measured by FFQ and 3DFR

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Physical Activity
Table 6: Descriptive statistics for energy expenditure (kJ/d) by AAS survey
Statistic N M ± SD SE Median IQR
Energy expenditure (kJ/d) - AAS 163 1098 ± 1054.00 83.00 500.00 829.00
Table 6 presents the detailed energy consumption of the participants in the study. The
comparison of mean and median energy intake by AAS survey indicated a highly negatively
skewed distribution. The results were compared with the energy intake of the 3-day diary in
Table 7. Descriptive summary in Table 7 reflected almost regular energy intake for the
participants, which was a markedly different result compared to energy intake summary by
AAS. Table 7 also indicated that energy expenditure by the participants was much higher
than energy consumed, measured in kilojoules per day. The energy balance row reported a
slightly negatively skewed distribution as the median was less than mean energy difference.
Table 7: Descriptive statistics for energy expenditure, energy intake, and energy balance (3-day diary)
Table 8: Paired t-test for difference between mean energy expenditure assessed via AASQ vs. 3DD
Variable M SE SD [95% Conf. Interval]
AAS Energy
Expenditure kJ/d 1109.99 85.9 1073 940 1280
3-day diary Energy
Expenditure kJ/d 8287.67 336.7 4205 7623 8953
Difference -7177.69 344.3 4301 -7858 -6497
N = 156, T= -20.8445, degress of freedom=155, P-value <0.001

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The difference in average energy expenditure or physical activity measured by AAS and
average 3-day diary (3DD) energy expenditure was done using a paired t-test. Table 8
presents the results of the paired t-test, which reflected that energy expenditure measured in
AAS scale was significantly greater than the measure by 3DD. The correlation by Pearson for
156 female participants reflected an almost zero correlation between average energy
expenditure (physical activity) measured by AAS and 3DD techniques. The correlation was
also statistically insignificant (t = 0.04, p = 0.64). The p-value of 0.64 indicated that there
was no noteworthy correlation whatsoever between the average physical activity measured by
AAS and 3DD. Hence, the null hypothesis, assuming that there was no correlation between
these measures failed to get rejected.
Figure 5: Scatterplot for AAS and 3-DEE of the Participants
Anthropometric Characteristics
Table 9: Descriptive statistics for Anthropometric Measurements
Statistic N M ± SD SE Median IQR
Height (cm) 173 164.9 ±7.2 0.5 165.0 10.0
Weight (kg) 173 61.8 ±11.9 0.9 60.0 12.0
BMI (kg/m2) 173 22.6 ±3.6 0.3 21.9 3.9
Waist Circumference (cm) 170 74.2 ±11.6 0.9 74.0 11.0

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Table 9 describes the Anthropometric measurements of the participants. Some missing values
were observed from the number of observations in column 2. Distribution of height, weight,
BMI, waist circumference was found to be almost normal. Comparative study for waist
circumference (WC) and BMI was conducted with cut off levels in Table 10 and Table 11.
Majority of the females in the sample was found to be maintaining a healthy BMI and
physical health. Almost 18% of women were found to either overweight or obese. A similar
analysis concerning WC cut-points reflected a resembling outcome. Waist sizes of nearly
75% of females were found to be below 80 centimeters. Rest of the 25% of women were
above the 80 centimeters cut off point and were the same participants whose BMI was above
25 kg/m2.
Table 10: Proportion of adults by BMI category according to WHO BMI classification
Table 11: Proportion of adults who exceed WC cut-points
WC Category N Percent (%)
WC <80 cm 127 74.7
WC=>80 cm 30 17.7
WC =>88 cm 13 7.7
Total 170 100.0
Proportion of adults who exceed WC cut-points

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A paired t-test analysis was done to assess the difference in fat, measured by Bioelectrical
Impedance Analysis (BIA) and skinfold measurement, for 164 participants. Table 12 reflects
the results, where the p-value of 0.003 was less than 0.05 (5% level of significance). The p-
value indicated that there was a statistically significant difference in fat measured by the two
methods, and percentage fat measured by skinfolds was significantly higher than that of the
measures of the BIA method.
Table 12: Paired t-test for Difference between percent body fat assessed by skinfolds vs. BIA
Variable M Std. Err. SD [95% Conf. Interval]
% body fat from skinfolds 26.3 0.7 8.8 24.9 27.6
% body fat from BIA 23.8 0.5 6.8 22.7 24.8
Difference 2.5 0.7 8.7 1.2 3.8
N = 164, T= 3.67, degress of freedom=163, P-value=0.003
Pearson’s correlation was used for finding any possible Correlation between BIA and
skinfold measures of percentage fat. The analysis yielded a mid-positive Correlation between
the two methods, but the significance or p-value of the Correlation was 0.64 (> 0.05). Hence,
at 5% level, the null hypothesis assuming no correlation between the two methods failed to
get rejected. Figure 6 scatterplot presents the pairwise plotting of percentage fat measured by
BIA and skinfold method. The trend reflected a linear relation between the factors, but the
apparent relationship was found to be statistically insignificant.
Figure 6: Scatterplot for Correlation between % body fat from skinfolds vs. BIA

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Discussion
Findings
Dietary intake
A statistically significant greater intake of calcium measured by 3-days of food records
compared to the FFQ scale was noted (10). A positive correlation between the intake of
calcium measured by FFQ and 3DFR was present, but the relationship was not statistically
significant. This study compared strategies and dietary consumption to assess the nutritional
fitness of women. Compared to 3-days of dietary intake, the FFQ experience in this
population was negative (2). The document also emphasizes that the consumption of calcium
in women was less than the proportion of the cut points, such as the level of adequacy of the
assessment of the EAR and its probability (3). Average Calcium (M = 726.9 mg/d) intake
was less than recent recommendations of Nutrient values for 19-30 years of women
(Calcium = 840 mg/d). The scenario for Folate (M = 391.2 mg/d), Iron (M = 11.7 mg/d),
Vitamin-C (M = 105.1 mg/d), and Zinc (M = 10.9g/d) consumptions were above the EAR
estimated cut off points (Vitamin C 30mg/d, Folate 320mg/d, Zinc 6.5mg/d, Iron 8mg/d).
Physical activity
Pearson correlation reflected almost zero correlation between the average energy
consumption (physical activity) measured by AAS and 3DD methodology (16). Average
energy expenditure measured by 3-day dairy (3DD) (M = 8290 KJ/d) and AAS (M = 1098
KJ/d) for the females were considerably greater than the AASQ recommendations for
physical activity (M = 480 KJ/d) (8). Noticeably, average energy spent by 3DD (M = 8290
KJ/d) was noted to be greater than energy intake (M = 7525 KJ/d) by 3DD, which indicated
a negative energy balance (1).
Anthropometric characteristics
The difference in body fat measured by the two methods was statistically significant, and the
percentage of fat measured by skin folds was significantly higher than that of the BIA method
(9). The energy consumption in the AAS scale was substantially higher than the measured
value of 3DD, and most of the women in the sample proved to have maintained a healthy
BMI and good health. Almost 18% of women appear to be overweight or obese. Women with
a waist size of nearly 75% were found to be less than 80 cm cut off point (17).

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Limitations
An appropriate approach would be to provide information on the number of people with an
increased risk of under-utilization. Limited information in the nutritional data on the
suitability of individual costs was there as the data can be falsified, particularly among the
female population, which was very expensive or limited, and to achieve or maintain low
weight and fat. The study did not discuss details of the impact of total daily energy intake on
the obese and overweight groups. Since this was a cross-sectional study focusing on the
absorption of food intake and human measurements, there was no effective presumption of
the causes and effects of obesity. The consumption of nutrition can be a more urgent need for
a longitudinal study.
Conclusion
Compared to the FFQ scale, calcium intake has significantly increased statistically, 3-days of
food records. FFQ correlated positively with the consumption of calcium measured with
3DFR but was not statistically significant. Calcium intake in women was below a fraction of
a reduction point, such as the adequacy of hearing ratings. The correlation coefficient reflects
a nearly zero correlation between the average energy consumption (physical activity)
measured by the AAS and the 3DD method. The difference in adipose tissue, measured by
two ways, was statistically significant and the proportion of fat measured by wrinkles on the
skin was significantly higher than that of the BIA method. The energy consumption on the
AAS scale was substantially higher than the measured value of 3DD, and most of the women
in the sample were found healthy and healthy.
Future Research
The validation of FFQs of the female population also includes the relevant areas for
determining the volume of food used and the proper presentation of the food types. In this
study, there was no accurate measurement of physical activity to detect dietary data. Also,
nutritional testing and dietary limitation can also help to assess the effectiveness of nutrition
records. The final FFQ restrictions used in this study also analyze finite nutrients in
inadequate nutrient data, such as iodine and fatty acids, as well as mineral substances that
need iron and zinc. In the current research, dietary dependence and eating habits were
categorized as 72 hours for food and beverage registrations, a common research-based
approach among large populations. The diet record of 72 hours is not always representative
of regular consumption. These shortcomings should also be addressed in the future.

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Appendices
Table 13: DESCRIPTIVE STATISTICS FOR NUTRIENT INTAKE
Statistic
Energ
y
intake
(kJ/d) -
3-day
food
record
Daily
%
energ
y
from
fat -
3-day
food
recor
d
Daily %
energy
from
carbohydra
te - 3-day
food record
Daily
%
energ
y
from
protei
n - 3-
day
food
recor
d
Daily
%
energ
y
from
alcoh
ol -
3-day
food
recor
d
Calciu
m
intake
(mg/d)
- 3 day
food
record
Iron
intak
e
(mg/
d) - 3
day
food
recor
d
Folat
e
intak
e
(ug/d)
- 3
day
food
recor
d
Vitami
n C
intake
(mg/d)
- 3
day
food
record
Zinc
intak
e
(mg/
d) - 3
day
food
recor
d
FFQ
Calciu
m
(mg/d)
FFQ
Dairy
(serves/
d)
Number (N) 173 173 173 173 173 173 173 173 173 173 173 173
mean 7525.5 33.5 40.1 21.2 1.0 726.9 11.7 391.2 105.1 10.9 630.7 2.1
standard deviation (SD) 2421.1 9.0 11.3 8.2 2.7 344.3 5.8 180.1 72.1 7.7 447.6 1.5
standard error (SE) 184.1 0.7 0.9 0.6 0.2 26.2 0.4 13.7 5.5 0.6 34.0 0.1
p25 5990.0 29.0 34.0 17.0 0.0 535.0 8.0 257.0 57.0 7.0 318.0 1.1
p50 (median) 7257.0 33.0 40.0 20.0 0.0 708.0 11.0 374.0 91.0 9.0 511.0 1.7
p75 8669.0 39.0 46.0 23.0 0.0 881.0 14.0 487.0 132.0 12.0 935.0 3.1
The interquartile range
(IQR) 2679.0 10.0 12.0 6.0 0.0 346.0 6.0 230.0 75.0 5.0 617.0 2.1
min 2010.0 8.0 11.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
max
16674.
0 69.0 97.0 87.0 15.0 2373.0 46.0
1000.
0 406.0 50.0 2131.0 7.1
Table 14: Probability approach for Iron
Percentiles of
Requirement
Distribution
Range usual
intakes assoc
with percentiles
Risk of
inadequate
intakes
No.
with
intake
in
range
No x risk
2.5 1 1 1 1
5 3 0.96 1 0.96
10 5 0.93 13 12.09
20 7 0.85 16 13.60
30 8 0.75 22 16.50
40 9 0.65 16 10.40
50 10 0.55 15 8.25
60 11 0.45 13 5.85
70 13 0.35 28 9.80
80 16 0.25 25 6.25
90 20 0.15 11 1.65
95 26 0.08 8 0.64
97.5 34 0.04 2 0.08
100 46 0 2 0
Total 173 87.07
Divide by N=173 (87.07/173) x 100
50.33

21
Table 15: Descriptive statistics for anthropometric measurements
Statistic Height
(cm)
Weight
(kg)
BMI
(kg/m2)
Waist
Circumferen
ce (cm)
% body
fat
from
skinfold
s
%
bod
y fat
fro
m
BIA
Number (N) 173 173 173 170 170 164
mean 164.9 61.8 22.6 74.2 26.4 23.8
standard deviation (SD) 7.2 11.9 3.6 11.6 8.7 6.8
standard error (SE) 0.5 0.9 0.3 0.9 0.7 0.5
p25 160.0 54.0 20.2 69.0 23.0 19.0
p50 (median) 165.0 60.0 21.9 74.0 26.5 23.0
p75 170.0 66.0 24.1 80.0 30.0 28.0
The interquartile range
(IQR) 10.0 12.0 3.9 11.0 7.0 9.0
min 148.0 41.0 15.4 32.0 2.0 6.0
max 187.0 106.0 38.5 118.0 73.0 41.0

22
Practical Data
Practical Data 1
Table 1.1
Food Item Serve size Ca (mg)
How many
times per
day
How many
times per
week
Rarely or
never
Your
calcium
intake
(mg)
Milk
Whole 1 glass = 200 ml 235 mg 0.5 3.5 117.5
Low-fat 1 glass = 200 ml 273 mg  0
Skim 1 glass = 200 ml 333 mg  0
Soy milk – fortified 1 glass = 200 ml 235 mg  0
Milk in tea or coffee 1 Tbsp. = 20 ml
Calculate
from
above
2 14 47
Milk with cereal ½ cup (125 ml)
Calculate
from
above
1 7 146.875
Cheese
1 slice hard cheese e.g.
tasty 20 g slice 148 mg 0.5 3.5 74
Soft, cream, cottage, Brie 1 Tbsp. = 20 g 58 mg 1 7 58
Yoghurt
Whole, low fat, fruit, plain 1 carton – 200 g 335 mg 1 7 335
Ice cream 1 scoop – 50 g 64 mg 0.5 3.5 32
Total number serves per
day 2 14 600
Seafood
Fish 1 fillet 24 mg 2 14 48
Tinned salmon with bones ½ cup = 120 g 192 mg 0.5 3.5 96
Tinned sardines with
bones 4–5 = 60 g 196 mg  0
Pages for data entry – Short food frequency questionnaire
Total daily calcium intake from FFQ (unadjusted) = 954.375 mg
Adjust for the additional 40% intake form the remainder of the diet by using the following formula:
Total daily calcium intake from FFQ × 100/60 = 1590.6 mg
Total daily calcium intake from dairy = 600 mg
Total number of serves of dairy per day = 2

23
Practical Data 2
Pages for data entry – Three-day food record HSN719
Date 17/12/2018 Day of the week 1
Time Type of
food/drink
Household
measures (cups,
tablespoons,
teaspoons),
natural unit sizes
(slices), or
dimensions (cm)
Weight (g)
Amount
left (g or
other
measure)
Amount
eaten (g or
other
measure)
7:00 AM Coffee 1 Cup 100 10 90
Cream 1 Tbsp 15 0 15
Sugar 2 tsp 12 2 10
9:00 AM Water 1 cup 200 0 200
11:00 AM Bread 3 slices 75 0 75
Turkey 2 oz. 100 0 100
Mayo 1 Tbsp 15 2 13
Lettuce 2 leaves 6 0 6
11:30 AM Water 2 cups 400 0 400
1:30 PM Cheese 1 cub.inch 20 2 18
Apple 1 big 80 0 80
wheat
crackers 8 40 0 40
4:00 PM Muffin 2 160 20 140
Tea 1 cup 60 10 50
7:30 PM White
Bread 2 130 0 130
Hamburger
Grilled 1 patty 150 10 140
Tomato
raw 1 50 15 35
Ketchup 2 Tbsp 25 5 20
10:00 PM Water 2 cups 400 0 400

24
Pages for data entry – Three-day food record HSN719
Date 20-12-2018 Day of the week 4
Time Type of
food/drink
Household
measures (cups,
tablespoons,
teaspoons),
natural unit sizes
(slices), or
dimensions (cm)
Weight (g)
Amount
left (g or
other
measure)
Amount
eaten (g or
other
measure)
7:00 AM Coffee 1 Cup 100 10 90
Cream 1 Tbsp 15 0 15
Sugar 2 tsp 12 2 10
9:00 AM Water 2 cup 400 0 400
11:00 AM Bread 2 slices 75 0 75
Chicken 1oz. 70 0 70
Ketchup 1 Tbsp 10 2 8
Lettuce 1 leaf 4 0 4
11:30 AM Water 2 cups 400 0 400
1:30 PM Cheese 1 cub.inch 20 2 18
Juice 1 cup 100 0 100
rice 1 cup 50 0 50
4:00 PM Cake 1 85 10 75
Tea 1 cup 60 10 50
7:30 PM White
Bread 2 130 0 130
Hamburger
Grilled 1 patty 150 10 140
Tomato
raw 1 50 15 35
Ketchup 2 Tbsp 25 5 20
9:00 PM Ice-cream 1 cup 100 0 100
10:00 PM Water 2 cups 400 0 400

25
Pages for data entry – Three-day food record HSN719
Date 23-12-2018 Day of the week 7
Time Type of
food/drink
Household
measures (cups,
tablespoons,
teaspoons),
natural unit sizes
(slices), or
dimensions (cm)
Weight (g)
Amount
left (g or
other
measure)
Amount
eaten (g or
other
measure)
8:00 AM Coffee 1 Cup 100 10 90
Cream 1 Tbsp 15 0 15
Sugar 2 tsp 12 2 10
10:00 AM Beer 12 oz 750 0 750
11:00 AM Rice 1 cup 75 0 75
Beef Steak 1oz. 70 0 70
Chicken
Soup 1 cup 50 5 45
Lettuce 1 leaf 4 0 4
11:30 AM Water 2 cups 400 0 400
1:30 PM Mayo 1 Tbsp 15 0 15
Juice 1 cup 100 0 100
4:00 PM Cake 1 85 10 75
Coffee 1 cup 70 5 65
7:30 PM Brown
Bread 2 130 0 130
Prawn
Grilled 2 pcs 100 20 80
Tomato
raw 1 50 15 35
9:00 PM Ice-cream 1 cup 100 0 100
10:00 PM Water 2 cups 400 0 400

26
Practical Data 3
Sl No Frequency Measure Type
1 6 times Walking
2 125 minutes Walking
3 1 times Vigorous
4 50 minutes Vigorous
5 4 times Vigorous
6 100 minutes Vigorous
7 2 times Moderate
8 70 minutes Moderate
Gender Male
Weight 75 Kg
Age 29 years
Total Physical Activity 495 minutes /week
A Sufficient
Total Physical Activity 495 minutes /week
Frequency 13
B Sufficient
Physical activity
energy expenditure 1226.25 KJ /day
C Sufficient
The Active Australian Survey
Practical Data 4
Stride Length 60 cm 0.0006 km
Body Weight 75 kg
STEPS Pedometer distance (km/day) Energy (MET.min/day) KCAL KJ*
DAY 1 11455 6.9 295.54 387.89 1629.2
DAY 2
(optional) 9875 5.9 254.78 334.39 1404.4
DAY 3
(optional) 13554 8.1 349.69 458.97 1927.7
AVERAGE 11628.00 6.98 300.00 393.75 1653.76
Sufficient

27
Practical Data 5
DAY 1: Monday DATE: 17/12/2018
Minute
Hour
0 1 ----------- ----------- 2
1 ----------- ----------- ----------- -----------
2 ----------- ----------- ----------- -----------
3 ----------- ----------- ----------- -----------
4 ----------- ----------- ----------- -----------
5 ----------- ----------- ----------- -----------
6 ----------- ----------- 1 -----------
7 ----------- ----------- ----------- -----------
8 ----------- ----------- ----------- -----------
9 2 4 4 2
10 5 ----------- ----------- -----------
11 ----------- ----------- ----------- -----------
12 ----------- ----------- ----------- 2
13 ----------- ----------- 5 -----------
14 2 ----------- ----------- -----------
15 ----------- ----------- 2 -----------
16 ----------- ----------- ----------- -----------
17 ----------- ----------- 3 2
18 ----------- ----------- 4 -----------
19 3 ----------- ----------- -----------
20 ----------- ----------- ----------- -----------
21 8 ----------- ----------- 3
22 ----------- ----------- 6 -----------
23 ----------- ----------- ----------- 1
0–15 16–30 31–45 46–60

28
DAY 2: Thursday DATE: 20/12/2018
Minute
Hour
0 ----------- ----------- 1 -----------
1 ----------- ----------- ----------- -----------
2 ----------- ----------- ----------- -----------
3 ----------- ----------- ----------- -----------
4 ----------- ----------- ----------- -----------
5 ----------- ----------- ----------- 2
6 1 2 3 -----------
7 ----------- 2 ----------- -----------
8 ----------- ----------- 3 -----------
9 ----------- ----------- 2 -----------
10 5 ----------- ----------- -----------
11 ----------- ----------- ----------- -----------
12 ----------- ----------- ----------- 2
13 ----------- ----------- 5 -----------
14 ----------- ----------- ----------- -----------
15 ----------- ----------- 2 -----------
16 6 7 5 6
17 ----------- 2 ----------- -----------
18 ----------- 4 ----------- -----------
19 5 ----------- ----------- -----------
20 ----------- ----------- ----------- -----------
21 ----------- 3 ----------- 4
22 ----------- ----------- 4 -----------
23 ----------- ----------- ----------- 1
0–15 16–30 31–45 46–60

29
DAY 3: Sunday DATE: 23/12/2018
Minute
Hour
0 ----------- ----------- ----------- -----------
1 ----------- ----------- ----------- -----------
2 ----------- ----------- ----------- -----------
3 ----------- ----------- ----------- -----------
4 ----------- ----------- ----------- -----------
5 ----------- ----------- ----------- -----------
6 ----------- ----------- ----------- -----------
7 ----------- ----------- ----------- -----------
8 5 4 6 4
9 ----------- 2 ----------- -----------
10 4 ----------- ----------- -----------
11 ----------- ----------- ----------- -----------
12 ----------- 2 ----------- -----------
13 ----------- ----------- 1.5 -----------
14 ----------- ----------- ----------- -----------
15 5 6 4 5
16 ----------- 2 ----------- -----------
17 ----------- ----------- 4 2
18 ----------- ----------- 1.2 -----------
19 2 ----------- ----------- -----------
20 ----------- 2 1.5 3
21 ----------- 2 ----------- -----------
22 ----------- ----------- ----------- -----------
23 ----------- 1.5 ----------- -----------
0–15 16–30 31–45 46–60

30
Total BMR 7.621
Estimate the average
1 (_____× 15 × 1 × BMR)/1440 =_______ 0.2381563 (_____× 15 × 1 × BMR)/1440 =_______ 0.238156 (_____× 15 × 1 × BMR)/1440 =_______ 0
1.2 (_____× 15 × 1.2 × BMR)/1440 =______ 0 (_____× 15 × 1.2 × BMR)/1440 =______ 0 (_____× 15 × 1.2 × BMR)/1440 =______ 0.095263
1.5 (_____× 15 × 1.5 × BMR)/1440 =______ 0 (_____× 15 × 1.5 × BMR)/1440 =______ 0 (_____× 15 × 1.5 × BMR)/1440 =______ 0.357234
2 (_____× 15 × 2.0 × BMR)/1440 =______ 1.1113958 (_____× 15 × 2.0 × BMR)/1440 =______ 1.111396 (_____× 15 × 2.0 × BMR)/1440 =______ 1.111396
2.5 (_____× 15 × 2.5 × BMR)/1440 =______ 0 (_____× 15 × 2.5 × BMR)/1440 =______ 0 (_____× 15 × 2.5 × BMR)/1440 =______ 0
3 (_____× 15 × 3.0 × BMR)/1440 =______ 0.7144688 (_____× 15 × 3.0 × BMR)/1440 =______ 0.714469 (_____× 15 × 3.0 × BMR)/1440 =______ 0.238156
4 (_____× 15 × 4.0 × BMR)/1440 =______ 0.952625 (_____× 15 × 4.0 × BMR)/1440 =______ 0.952625 (_____× 15 × 4.0 × BMR)/1440 =______ 1.587708
6 (_____× 15 × 6.0 × BMR)/1440 =______ 0.4763125 (_____× 15 × 6.0 × BMR)/1440 =______ 0.952625 (_____× 15 × 6.0 × BMR)/1440 =______ 0.952625
7 (_____× 15 × 7.0 × BMR)/1440 =______ 0 (_____× 15 × 7.0 × BMR)/1440 =______ 0.555698 (_____× 15 × 7.0 × BMR)/1440 =______ 0
10 (_____× 15 × 10.0 × BMR)/1440 = ____ 0 (_____× 15 × 10.0 × BMR)/1440 = ____ 0 (_____× 15 × 10.0 × BMR)/1440 = ____ 0
Total_______MJ 3.4929583 Total_______MJ 4.524969 Total_______MJ 4.342382
Average MJ/day: 4.1201
Average KJ/day: 4120.10
Enter your 3-day diary data below (use your individual BMR calculated previously)

31
Practical Data 6
Table 6.2
Anthropometry Observer 1 Observer 2 Observer 3 Mean Reference range
Body Mass (kg) 75 74.98 75 74.99 65.3 - 79.8 kg
Height (cm) 175.26 175.38 175.32 175.32 170-180
Waist circumference (cm) 86.36 86.18 86.3 86.28 <94 cm
Mid-upper arm circ. (cm) 30.2 30.18 30.3 30.23 28-33
Anthropometric assessments of your body by three separate observers: