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thesis entitled

EFFECT OF AN INTERACTIVE
COMPUTERIZED DIET ANALYSIS
PROGRAM IN NUTRITION EDUCATION

presented by

Sheryl Ann Nied

has been accepted towards fulfillment
of the requirements for

M.S. degfiwin Human Nutrition

 

 

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EFFECT OF AN INTERACTIVE COMPUTERIZED DIET
ANALYSIS PROGRAM
IN NUTRITION EDUCATION
BY

SHERYL ANN NIED
A THESIS

Submitted to
Michigan State University
in fulfillment of the requirements
for the degree of

MASTER OF SCIENCE

Department of Food Science and Human Nutrition

1991

ABSTRACT

EFFECT OF AN INTERACTIVE COMPUTERIZED DIET
ANALYSIS PROGRAM
IN NUTRITION EDUCATION

BY

SHERYL A. NIED

This study examined whether self-assessment of food
intake via a computerized diet analysis program
enhanced improvement in nutrition knowledge, dietary
attitudes and intakes over that expected of students in
an introductory nutrition course at MSU. One of two
sections (experimental) self-evaluated individual food
intake by a computerized diet analysis program; the
second section (control) submitted food records without
receiving feedback until the end of the study. Both
sections received we and posttests on nutrition
knowledge and attitudes. Significant improvement
(p<0.05) in nutrition knowledge was seen in both groups
from pretest to posttest for the students that
participated (Control, n=177; Experimental, n-77). No
significant differences were found in either attitudes or
dietary intakes between the two groups. Further studies
are needed to determine whether self-evaluation of
intakes is consistent with students' interests, and to
identify an educational approach that can improve
dietary behaviors as well as increase knowledge.

ACKNOWLEDGMENTS

There are many people who helped me along the way in my graduate
studies and research who I would like to thank. First of all, thanks
to Mary Greely, Dana Wassmer, Cheryl Bates, and Debbie Gould, who
offered their time, advise, expertise and friendship when it was
needed most. Thanks also to Carol Friesan, Gail Moore, Amy Riley,
and Marci Askegard for their friendship and support. To the best
secretary and friend, Mary Schneider, thanks for alway being there
when I needed someone to talk to. Her advice, knowledge, never-
ending support, and ability to put things in perspective were the
things that got me through some of those days. I would to thank my
guidance committee members, Dr. Hoerr, Dr. Bond, and Dr. Heusner.
They were supportive, patient, and always willing to take time for
me, and understood that l was a wife and mother as well as a
graduate student. To my advisor, Dr. Song I owe the greatest debt.
Her first interest was always in me as a person, and my family, and
then me as a graduate student. Her support for me never waned, even
though my timeline took much longer than planned. She offered
advice and friendship and much more, and for all those extras I will
always be grateful. She helped me believe in myself and my
abilities, and challenged me to meet and exceed her highest
expectations. I thank my son Benjamin for brightening my days, and
for the ability to put a smile on my face on those days when nothing
seemed right. And to sweet Alex, who arrived earlier than expected
and put the kink in my timeline, but has made every day since then
worthwhile. Most of all, thanks to my husband Dan for being my best
supporter and helper. Without his computer expertise, I would
probably still be typing. He has always encouraged me to reach my
goals, motivated me, believed in me, and been understanding,
patient, and kind. Thanks also to the W. K. Kellogg Foundation for
their funding of this research project as a sub-project of the
campus-wide Healthy U Health Promotion Program.

iii

TABLE OF CONTENTS

Introduction ................................................................................................................ 1
Literature Review ........................................................................................................ 5
Relationships Among Knowledge, Attitudes and Dietary
Behavior .......................................................................................................... 5
A) Positive Relationships Among Measurements ........................................ 5
B) Mixed Relationships Among Measurements ........................................... 10
C) Little or No Relationship Among Measurements ..................................... 1 3
Dietary Assessment Methods ............................................................................ 18
A) 24-Hour Recall ..................................................................................... 19
B) Food Intake Record .............................................................................. 22
C) Food Frequency Form .......................................................................... 25
D) Concerns With Dietary Assessment Methods ......................................... 26
Methods For Assessing Diet Quality ................................................................... 31
A) Nutrients I 1,000 Kilocalories ................................................................. 31
B) Index of Nutritional Quality (INC) ............................................................ 32
C) Nutrient Adequacy Ratio (NAR) and Mean
Adequacy Ratio (MAR) ....................................................................... 33
D) Food Groups Methods .......................................................................... 36
Variables related to dietary intake ........................................................................ 36
Computers In Education .................................................................................... 38
Methods ..................................................................................................................... 45
Subjects .......................................................................................................... 45
Instruments ...................................................................................................... 45
Procedures ...................................................................................................... 49
A) Data Collection ..................................................................................... 49
8) Statistical Procedures ........................................................................... 51
Results and Discussion ............................................................................................... 54
Subjects .......................................................................................................... 54
Nutrition Knowledge ......................................................................................... 56
Nutrition Attitudes ............................................................................................. 58
Dietary Intake .................................................................................................... 60
Nutritional Adequacy ........................................................................................ 7O
Variables Related to MAR .................................................................................. 72
Relationship Among Nutrition Knowledge. Attitudes
and Dietary Intake ............................................................................................. 75
Assumptions .............................................................................................................. 79
Limitations .................................................................................................................. 81
Summary .................................................................................................................... 84
Conclusions ............................................................................................................... 86
Recommendations ...................................................................................................... 87
Appendices ................................................................................................................ 90
Appendix A: UCRIHS letter ............................................................................... 90
Appendix B: Consent Forms for HNF 102 Students
(Sec. 2 and Sec. 3) ................................................................... 91
Appendix C: Nutrition Knowledge and Attitudes
Pretest and Posttests .............................................................. 93
Appendix D: One-Day Diet Record Assignments: '
(Sec. 2 and Sec. 3) ................................................................... 109

1V

Appendix E: Summary of knowledge-attitude-behavior

relationships: A review of literature cited ..................................... 114
Appendix F: Mean daily nutrient intake: means and standard
deviations ................................................................................ 1 19
Appendix G: Kraemer and Thiemann Power analysis
sample equation ....................................................................... 122
Appendix H: SPSSaX data program ................................................................... 123
References ................................................................................................................ 1 27

LIST OF TABLES

Table 1 - Class level of students ................................................................................... 55
Table 2 - Place of residence of students ....................................................................... 55
Table 3 - Percent of males and females in the study group ............................................. 56
Table 4 - Knowledge test scores .................................................................................. 57
Table 5 - ANOVA for knowledge test means ................................................................. 57
Table 6 - Attitude test scores ........................................................................................ 58
Table 7 - Mean daily nutrient intake ............................................................................... 61
Table 8 - Results of ANOVA for tolacin, alcohol, vitamin A, and magnesium
intake from pretest to posttest ................................................................ 60
Table 9 - Mean daily nutrient intake of females ............................................................... 65
Table 10— Results of ANOVA for carbohydrate, magnesium and saturated
fat intake of the females (n=201) ............................................................. 64
Table 11 - Mean daily nutrient intake of males ................................................................ 67
Table 12 - Results of ANOVA for calories, total fat, vitamin D, vitamin C,
sodium and calcium intake of the males (n=53) ......................................... 68
Table 13 - Pretest and posttest MAR of control and experimental groups ........................ 71
Table 14 - Pretest and posttest MAR tor females and males ............................................ 71
Table 15 - Results of the regression analysis for the variables gender and
age to predict pretest MAR ..................................................................... 72
Table 16 - Percentage of the total study population by age groups ................................. 73
Table 17 - Results of the regression analysis for the variable gender to predict
posttest MAR ..................................................................................... 74
Table 18 - Correlations between nutrition knowledge, attitudes and
dietary intake ...................................................................................... 75
Table 19 - Summary of knowledge-attitude-behavior relationships:
A review of literature cited ................................................................... 114
Table 20 - Mean daily nutrient intake: means and standard deviations ............................. 119
Table 21 - Mean daily nutrient intakeot females: means and standard deviations .............. 120
Table 22 - Mean daily nutrient intake of males: means and standard deviations ................ 121

vi

CHAPTER ONE

INTRODUCTION

An introductory nutrition course (HNF 102) at Michigan State
University (MSU) attracts 800-1000 students every quarter who
enroll in two live-lecture sections and a TV videotape section. When
surveyed about why they registered for the course, more than 80% of
the students responded that they wanted to use the nutrition
information in their daily lives and for their families. This
indicates that students are seeking nutrition information which can
be used to promote their own health. However, because of the size
of the class, there is limited interaction between instructors and
students for individualized help on Specific topics of concern.

In order to increase students' interest in the course and
awareness of the adequacy of their diets, an assignment was given
in the past for the students to record and hand-calculate their .
dietary intake for one day. The students were not required to hand in
the assignment, nor did they receive feedback. Many students
reported that the assignment would have been more meaningful to
them if they had feedback on the assessment of their intake and how
to make changes when needed. This suggested that although the
students were exposed to the nutrition information and were
expected to be increasing their knowledge, they were not
successfully interpreting this knowledge and translating it into
specific dietary behaviors.

Because one theory of nutrition education is that knowledge is
linked to behavior, many studies have been conducted to determine
whether this relationship actually exists, or to what extent it
exists. In these studies dietary intake was assumed to represent
behavior. Findings from some studies have supported a positive
relationship between nutrition knowledge and dietary intake
(Amstutz and Dixon, 1986; Brush et al., 1986; Foley et al., 1983;
Rosander and Sims, 1981; Soliah et al., 1983). In other

-1

2

investigations little or no correlation has been found (Douglas and
Douglas, 1984; Guiry and Bisogne, 1986; O'Connell et al., 1981; Ross,
1984; Shepherd and Stockley, 1987). Attitudes, rather than
knowledge were indicated as better predictors of dietary intake in
some studies (Carruth et al., 1977; Jalso et al., 1965; Schwartz,
1975), but again other studies have disputed these findings (Guiry
and Bisogne, 1986; O'Connell et al., 1981; Shepherd and Stockley,
1987)

The most prevalent relationship found within the knowledge-
attitude-behavior pathway was a positive correlation between
nutrition knowledge and attitudes (Carruth et al., 1977; Foley et al.,
1983; Grotkowski and Sims, 1978; Hart et al., 1990; Perron and
Endres, 1985; Schwartz, 1975; Soliah et al., 1983; Werblow et al.,
1978). Because of the supporting literature, this researcher
hypothesized that for this study at least knowledge and attitudes
would be correlated. This researcher also made the assumption that
for this study knowledge or attitudes alone might not be enough to
influence dietary behaviors, but if knowledge and attitudes both
improved, this improvement might have a positive effect on dietary,
intake. It is recognized that many variables, along with knowledge
and attitudes, have an effect on dietary intake. These variables
include at least gender (Beerman et al., 1990; Haseba and Brown,
1968), body weight (Wakefield and Miller, 1971), length of time in
college (Brown, 1967), subscription to a meal plan (Gottschalk et al.,
1977; Jakobovits et al., 1977; O'Leary and Lee, 1975), patterns
established prior to attending college (Stasch et al., 1970), place of
residence while in college (Beerman et al., 1990, O'Leary and Lee,
1975), education (Young et al., 1956), the symbolic meaning attached
to food (Pumpian-Mindlin, 1954), economic factors (Wilhelmy et al.,
1950), other family members (Dickens, 1965), advertising (Holden,
1971), personal needs (Lowenburg et al., 1974), and the family life
cycle (Coughenour, 1972).. It is beyond the scope of this study to
examine these reported variables that influence dietary intake.
However,in light of the conflicting results from the studies of
relationships among knowledge, attitudes and behavior, it was
apparent that further research was needed to determine how and

3

whether nutrition educators can improve peeple's dietary intake by
increasing their nutrition knowledge and attitudes.

Though the computer has been used in nutrition education for
more than a decade, there is little information available on its
effectiveness in facilitating transfer of nutrition knowledge and
attitudes into positive behavior changes (Ries and Granell, 1985).
The research on the use of computers in education shows that in
general, computer-assisted instruction (CAI) is at least as effective
as traditional methods of education, and users generally have
positive attitudes toward CAI as a method of education (Magidson,
1977; Mathis et al., 1970). Previous articles (Gold and Duncan,
1980; Weinberg and Scott, 1983) supported the idea that computers
may serve well as motivational devices through the use of self-
assessment programs, and that self-monitoring, a strong behavior
change technique, is facilitated by the computer.

An interactive, user-friendly computerized diet analysis program
was developed at MSU. The program was used in this study to
provide the students in the introductory nutrition class the
opportunity to assess their own diets. The students were,
encouraged to use the program as many times as desired at their
own convenience for self-assessment or self-monitoring. The goal
of this study was, therefore, to determine if changes in students'
nutrition knowledge, attitudes and dietary behaviors occurred when
they received immediate computer feedback on their dietary intakes
and were encouraged to practice manipulating their diets. One group
of students (experimental group) completed one-day diet records and
used the computerized diet analysis program for self-assessment of
their dietary intake at least twice in one quarter. Another group
(control group) of students completed one-day diet records and
turned them in. The researcher completed the dietary assessments
and gave feedback on their diets at the end of the quarter.

The research hypothesis tested in this investigation was that
students‘ recording of one-day dietary intakes and use of the
computerized dietary analysis program for dietary assessment, as a
part of a regular introductory nutrition class, would heighten the

4

students' need for change and therefore would improve nutrition
knowledge and attitudes and affect dietary behaviors.

The specific objectives of this study were:

1.

To compare differences in the nutrition knowledge gained by
experimental and control groups of students during a quarter in
an introductory nutrition class at MSU.

To compare differences in the nutrition attitudes of experimental
and control groups of students during a quarter in an introductory
nutrition class at MSU.

To compare differences in the self-reported dietary intake of
experimental and control groups of students during a quarter of
an introductory nutrition class at MSU. and to compare
differences in the dietary intake of males and females.

To measure nutritional adequacy and identify demographic and
environmental variables that might be related to nutritional
adequacy.

To determine if there is a relationship among nutrition
knowledge, attitudes and dietary intake.

CHAPTER 'IWO
LITERATURE REVIEW

This chapter provides a review of the literature related to the
study hypothesis and objectives. The first section reviews research
regarding the relationship among nutrition knowledge, attitudes and
dietary behaviors. The second section includes literature pertaining
to the various methods used for dietary assessment. The third
section reviews the literature related to dietary scores for
assessing nutritional adequacy. The fourth section contains
information on variables related to dietary intake. The last section

is a review of the literature on the usefulness of computers in
educafion.

RELATIONSHIPS AMONG KNOWLEDGE, ATTITUDES AND
DIETARY BEHAVIOR

A. Positive relationships among measurements

Optimal health through dietary improvement is the ultimate goal
of nutrition education programs. To justify efforts and funding for
such programs, evaluation should measure whether participants
made needed diet changes by using knowledge and skills gained, and
whether the changes were temporary or permanent. Amstutz and
Dixon (1986) conducted such an evaluation on 129 homemakers in
Maryland enrolled in the Expanded Food and Nutrition Education
Program (EFNEP). Homemakers were taught lessons focusing on the
importance of food and the Four Food Groups, meal planning
and preparation, food buying and storage, community food resources,
gardening, food preservation and weight control. Lessons were given
on an individual or group basis in the home. A 24-hr recall was
administered to the homemakers upon enrollment, at 6-month
intervals during the program, and at a period 2 to 43 months after
graduation. A paired t-test showed that final diet scores (at the end

5

6

of enrollment period) were significantly higher (p<0.01) than the
initial diet score, reflecting dietary improvement during program
participation. The follow-up diet scores were significantly lower
(p<0.01) than the final diet score, indicating some regression
following graduation from the program. However, the follow-up
scores were higher than the initial scores, and the data indicated
that the graduates retained three-fourths of their diet scores
(p<0.01). This study supported the notion that people will improve
their diet to some extent when they acquire knowledge of what
constitutes a balanced diet, and other dietary guidelines, and
documented that this nutrition education program was beneficial for
individuals.

A survey conducted in 1979-80 assessed the nutrition knowledge,
attitudes and dietary practices of Kansas elementary teachers,
school food service personnel, and fifth-grade students. From the
results of the survey Foley et al. (1983) reported the nutrition
knowledge, attitudes, and practices of a random sample of the
students (ns103). A test instrument was administered consisting of
multiple choice knowledge questions, and items with responses on
2-point and 3-point Likert-type scales for measuring nutrition-
related attitudes and practices The knowledge section included 35
questions concerning basic vocabulary and concepts, nutrition
practices, food selection, and food preparation and storage
procedures. The 30 items for measuring attitudes and practices
were related to the areas of value of proper nutrition, food
selection, eating patterns, beverage consumption, advertising
claims, and school food. Analysis of variance (ANOVA) results
showed that individual differences among students accounted for
97% of the variance on nutrition-related attitude and practices
scores. Positive correlations were found between nutrition
knowledge scores and attitude scores, nutrition knowledge scores
and nutrition-related practices scores, and nutrition-related
attitude scores and practices scores. The positive correlations
found in this study between nutrition practices and knowledge and
attitudes may be related to the fact that practices were assessed by
subjects responding to statements about dietary practices. It may

.7

be easier to respond positively to a statement than to exhibit the
actual behavior. If dietary practices had been assessed by some
form of actual food intake record the findings may have been
different. Another explanation for the correlations might be that
some of the students were receiving nutrition instruction, and also
may have been influenced by the teachers, who served as role
models.

Soliah et al. (1983) used the same survey data to report on the
nutrition knowledge, attitudes, and dietary practices of the Kansas
elementary teachers (n-819). The test instrument for the teachers
contained 55 multiple choice questions for assessing nutrition
knowledge and 60 items which measured nutrition-related attitudes
and practices and nutrition education practices. ANOVA indicated
that differences in nutrition knowledge scores of the elementary
teachers were attributable to grade level taught, whether teachers
had completed one or more college/continuing education courses on
nutrition, and whether they were teaching nutrition at that time.
Results of the ANOVA for the nutrition-related attitudes and
practices and nutrition education practice scores indicated that
individual differences among the teachers accounted for 87 to 96%
of the variance. Significant (p<0.05) positive correlations were
found between nutrition knowledge scores and nutrition-related
attitude scores, nutrition knowledge scores and nutrition-related
practices scores, and nutrition-related attitude scores and
practices scores. Again, the positive correlations found in this
study between nutrition practices and knowledge and attitudes
might be influenced by the fact that practices were assessed by
subjects' response to statements about dietary practices, as
explained above for the fifth grade students. The findings might
also be influenced because some of the teachers (54%) were teaching
nutrition in the classroom so may have been more motivated to
incorporate their knowledge into food-related attitudes and
practices.

Rosander and Sims (1981) reported that in the studies that
demonstrated positive effects of nutrition education on dietary
intakes of adult pregnant women, investigators assumed that

 

8

knowledge gains were responsible for the improved diets. However
the authors agreed with some social psychologists who proposed
that behavior change may be dependent upon attitude change and
that, possibly, attitude change must precede both knowledge
acquisition and behavior change. Therefore Rosander and Sims
evaluated an affective-based nutrition education program which
encouraged learners to explore their feelings about food as well as
to acquire nutrition knowledge. Subjects were 23 women
participating in a WIC (Women, Infants, and Children) program in
Central Pennsylvania. The program consisted of three lessons
incorporating group activities, specific experiences and cognitive
concepts. The educational objective was to improve dietary
behavior of participants. The affective objective was to improve
nutrition-related attitudes with emphasis on self-esteem, personal
control, and decision-making ability. Results showed that the
instructed 'group showed significant improvement (p<0.05) in
nutrition knowledge, attitudes and behavior, whereas an
uninstructed control group showed no significant change in any of
the three areas. The authors concluded that among receptive
participants in this study, the affective-based approach to nutrition
educations was effective not only in improving knowledge and
attitudes, but also in improving dietary behavior, which is the most
important objective.

A similar study was conducted by Brush et al. (1986), elaborating
on the design of Rosander and Sims. One hundred and twenty six
subjects were recruited from the general adult population, and were
divided into two groups. The treatment group (n-59) participated in
a five-week (total of ten hours) nutrition education program, and the
other group served as a control (n=67). The objectives were learner-
centered, with measurable outcomes in the cognitive, affective, and
behavioral domains. Subjects were administered pre-, post-, and
retention tests on nutrition knowledge and attitudes. The knowledge
test assessed cognitive gain in relation to course concepts. The
attitude toward nutrition scale measured the degree of
flexibility/rigidity in nutrition attitudes. Self-reported 24-hr
recalls were also collected. T-tests showed that on the knowledge

9

test, the treatment group scores rose significantly from pro-test to
post-test (p<0.001), and dropped slightly but not significantly from
post-test to retention test. The control group scores remained
constant over the three tests. Dietary behavior, as measured by 24-
hr recall improved in the treatment group only (p<0.05). Nutrition
attitudes did not change in either group. The authors attributed lack
of significant improvement in attitudes to high pro-test scores and
insufficient time interval for demonstration of attitude change.
Hart et al. (1990) evaluated the effect of a nutrition education
module on the nutrition knowledge, flexibility of attitude toward
nutrition, and attitude about nutrition education of missionaries
during their orientation period. The authors also attempted to
determine if there was a link between nutrition knowledge,
flexibility of attitude toward nutrition and attitude about nutrition
education, and they explored the nature of the relationships among
nutrition knowledge, flexibility of attitude, and attitude about
nutrition education. Experimental (n-22) and control (n-19) groups
of missionaries completed we and posttests consisting of a
nutrition knowledge test, two scales of nutrition attitude .
(flexibility of attitude toward nutrition and attitude about nutrition
education), and several open-ended questions about nutrition
education in the mission field. The experimental group attended four
nutrition classes of one hour each. Classes dealt with basic
nutrition and nutrition through the life span. Results showed no
significant differences between pretest and posttest knowledge
scores for both control and experimental groups. However the
comparison of change in knowledge scores from pretest to posttest
between the experimental and control groups was significantly
different (p<0.01). There were no significant differences between
the mean scores of control and experimental groups at pretest or
posttest on the flexibility of attitude toward nutrition scale. There
were no significant differences between the control and
experimental groups at the pretest on the attitude about nutrition
education scale. At the posttest the experimental group showed
significantly higher (p<0.01) mean scores than the control group for
13 of the 26 attitude statements. Significant (p<0.01) positive

10

correlations were found at posttesting for the experimental group
between nutrition knowledge and attitudes about nutrition
education, between nutrition knowledge and flexibility of attitude
toward nutrition, and between flexibility of attitude toward
nutrition and attitudes about nutrition education. The control group
showed a significant (p<0.01) positive correlation between
flexiblilty of attitude toward nutrition and attitudes about nutrition
eduaction. Regression analysis was used to investigate the extent
to which knowledge and flexibility scores contriubted to the
prediction of attitude scores. Posttest analysis showed that for the
control group the flexibiltiy score was the only significant predictor
of attitude score. In the experimental group posttest analysis
showed that the knowledge score was a significant predictor of both
the attitude and flexibility scores.

B. Mixed relationships among measurements

Perron and Endres (1985) conducted a study to determine whether
there was a relationship between nutrition knowledge, attitudes and.
dietary intakes (calcium, iron, and vitamins A, C, and E) of 31 female
high school athletes. A questionnaire was administered which
consisted of knowledge and attitude statements related to general
nutrition and nutrition for the athlete. Dietary intake was recorded
by a 24-hr recall followed by a 48-hr food record. Analysis of the
data revealed that knowledge was significantly correlated with
attitude (r-0.52, p<0.01). However neither nutrition knowledge nor
attitudes were good predictors of dietary practices. The authors
reported that 81% of the subjects were unhappy with their present
weight and 73% wanted to lose weight. Therefore their food choices
may have been influenced by an eagerness to be thin. A study of 94
college-level female athletes found results similar to the above
study (Werblow et al., 1978). Nutrition knowledge was positively
correlated with attitude, and nutrition knowledge and attitudes
were not correlated with food patterns. Again it was emphasized
that these athletic women were most concerned about their weight
and utilized food patterns they felt were desirable for a weight-

11

control regimen. Werblow et al. concluded that food habits of
female athletes may be influenced by a desire to be thin rather than
by nutrition knowledge and attitudes, which was supported by the
study of Perron and Endres (1985).

Schwartz (1975) investigated the relationship between previous
enrollment in home economics courses and present nutrition
knowledge, attitudes and practices of 313 female high school
graduates from selected Ohio high schools. One hundred and
seventy-one of the graduates had been enrolled in home economics
courses, including food and nutrition, while 142 graduates had not
had such courses. A knowledge test consisted of statements related
to general knowledge of nutrition concepts, food composition,
misconceptions about food, and application of basic nutrition
principles. The attitude test measured attitudes toward nutrition
and eating habits, meal planning, and food preparation. Dietary
intake was assessed by analysis of a food frequency record of intake
of foods in seventeen food groups during a three-day period as
compared with the Basic 4 pattern. The author found that previous
enrollment in high school home economics courses with a unit in
food, nutrition, and health was not consistently associated with
scores attained in tests of nutritional knowledge, attitudes, and
practices. The graduates who had taken the home economics courses
achieved higher mean scores in tests of nutritional attitudes and
practices than did those who had not taken the courses but the
differences were not significant. Significant correlations were
found between nutrition knowledge and attitudes, and between
attitudes and practices, but no significant correlation between
knowledge and practices. This suggested that attitude may mediate
both knowledge and practices. That is, it is possible that in order to
gain knowledge and internalize it, one must have a positive attitude
toward receiving the information. Attitudes have an effect on
dietary practices, but other variables also influence practices. This
may explain why no direct relationship was found between
knowledge and practices. Therefore even though these young
females gained nutrition knowledge, and expressed a positive

12

attitude toward food and nutrition, they did not necessarily follow
practices reflecting their knowledge and attitudes.

, Grotkowski and Sims (1978), in a similar stUdy conducted with
64 senior citizens, supported the association between nutrition
knowledge and attitudes (r-0.51, p<0.001) and attitudes and dietary
intake (rs-0.31, p<0.05). They also found no significant association
between nutrition knowledge and dietary intake.

In a study by Jalso et al. (1965), questionnaires to test
nutritional opinions and practices were completed by 340 adult
subjects who were members of various community organizations in
New York State. The nutritional opinion questionnaire was deveIOped
to assess areas of food and nutrition in which misconceptions are
most prevalent. The questionnaire on nutritional practices was
related to use of food supplements, use of special ”health foods",
methods of weight control, special diets, and avoidance of certain
foods. A positive correlation (r=0.63) was found between opinions
and practices, indicating that nutrition opinions were reflected in
nutritional practices. Education was positively correlated with both
practices (r-0.21) and opinions (“0.34), and age was negatively ,
correlated with scores on practices (r--0.42) and opinions (r--0.44).
This indicated that as level of education increased, subjects had
more valid nutrition opinions and practices, and as age increased
(from 30 to 60 and over) valid nutrition opinions and practices
decreased. A multiple regression analysis was conducted, and age
was indicated as the most important variable affecting scores for
both opinions and practices. Therefore authors concluded that the
direct relationship between education and valid nutrition opinions
and practices reflected the influence of age rather than education
directly.

Carruth et al. (1977) conducted a study in the Missouri EFNEP to
determine whether a significant relationship existed between
nutrition knowledge, attitudes, personality traits, and nutrition-
related behaviors, and which of these variables was the best
predictor of nutrition-related behavior. Twenty-seven Nutrition
. Education Assistants (NEAs) were divided into treatment (n-9) and
experimental (n-18) groups. Both groups initially completed a

13

knowledge test related to weight modification concepts, an attitude
questionnaire measuring an individual's adaptability toward
changing nutritional practices, and a personality questionnaire
which measured adaptability/ flexibility in personality traits. The
experimental group then received five weekly training sessions on
weight modification concepts suitable for teaching disadvantaged
adults. After the training, both groups completed a post-test of
nutrition knowledge and the attitude and personality questionnaires.
Three types of nutrition-related behaviors were documented:
brochure requests for free literature from both experimental and
control NEAs, verbal affirmations of nutrition practices from
experimental NEAs (9.9., the NEA recommending the Basic 4 food
groups as a basis for menu planning), and observed nutrition-related
behaviors of experimental NEAs (9.9., the NEA eating a meal that
contained foods from at least three of the four food groups). A
multiple regression equation was generated to predict a composite
score for the three types of behavior (i.e., mail requests, verbal, and
observed). Forty-seven and 32 percent, respectively, of the variance
in the composite scores were attributed to age and to responses to
the rigid attitude statements. Age had a negative effect, as
increasing age was associated with decreasing composite scores.
Also attitudes, particularly those characterized as flexible, were
more prepotent predictors of nutrition-related behavior than
nutritional knowledge. Therefore, authors concluded that age and
attitude were the best predictors of behavior.

C. Little or no relationship among measurements

Shepherd and Stockley (1987) conducted a study to determine
whether nutrition knowledge predicted either attitudes or behaviors.
Participants were 210 men and women 16 to 65 years of age who
returned questionnaires distributed at an International Food
Exhibition in London. The questionnaire consisted of four sections.
The first section contained demographic questions on age, sex, and
occupation. Three nutrition knowledge questions pertained to fiber

 

14

content of foods, fat content, and caloric intake. A frequency of
food consumption section contained questions on meat, meat
products, butter/margarine, and milk. To assess attitudes,
questions based on the Fishbein and Ajzen model (1980) were
formulated. Within this model, attitudes were assessed in relation
to a behavior (e.g. eating a food) rather than an object (eg. the food
itself). Behavior was predicted by behavioral intention, i.e. the
decision to behave in a certain way, which was predicted by the
attitude toward the behavior and the subjective norm. The attitude
toward the behavior was whether the subject saw the behavior as
good or bad, pleasant or unpleasant, etc.. The subjective norm was
the social pressure to behave in a certain way. Their results
indicated that nutrition knowledge was not a good predictor of
attitude or behavioral intention. Subjects with high nutrition
knowledge scores did not report lower consumption of or have more
negative attitudes toward the high-fat foods. Thus, greater
knowledge did not necessarily result in a difference in attitudes or
behavior. It was found that the person's attitude toward eating the
foods proved to be a better predictor of behavioral intention. One .
explanation offered by the authors was that nutrition knowledge may
be related to general attitudes toward nutrition, but not to more
specific attitudes toward eating certain foods. That could account
for the lack of relationship between knowledge and the intake of
specific nutrients. A limitation of the study was that nutrition
knowledge was assessed by three questions. It is possible that this
was not enough information to show any kind of relationship.

A study by Guiry and Bisogne (1986) supported the assumption of
Shepherd and Stockley's (1987) about the need for specificity in
measurement instruments. The authors studied knowledge, attitudes
and practices of caffeine consumption in 63 young women 15 to 30
years old. The knowledge instrument consisted of statements about
dietary sources of caffeine, general health effects of caffeine
consumption, and specific health concerns regarding caffeine use
during pregnancy. The attitude instrument consisted of Likert-type
items that reflected positive or negative feelings about 1) it is safe
for pregnant women to consume caffeine, 2) it is hard to limit

15

caffeine consumption, and 3) nutrition is not important. Caffeine
consumption was assessed by 24-hr recall and a beverage frequency
form. The strongest relationship was found between caffeine
consumption and the attitude that "it is hard to limit caffeine
consumption” (r=0.64, p<0.001). A significant negative relationship
(r--O.46, p<0.001) was found between knowledge scores and the
attitude ”it is safe for pregnant women to consume caffeine".
However, no association was found between caffeine knowledge and
the general attitude ”nutrition is not important" and the attitude ”it
is hard to limit coffee consumption“. Also no relationship was found
between caffeine knowledge and caffeine consumption. Authors
concluded that the lack of specificity between the knowledge and
general attitude statements may account for their observation, and
that the specific attitude ”it is hard to limit caffeine consumption"
was an important predictor of consumption.

In a study of high school athletes (Douglas and Douglas, 1984), a
weak correlation (r-0.35) was found between knowledge and food
practices of 515 male athletes, and a weaker correlation (r-0.15)
for 425 female athletes. The authors reported that the male
athletes had higher food practice scores than the females because
they ate more food than the females. Therefore the stronger
correlation found between their knowledge and food practices was
probably not related to nutrition knowledge but to the fact that
young men eat more, and so are more likely to consume foods from
the four food groups.

Ross (1984) measured change in knowledge and attitudes of
nursing students after completing a nutrition course. She found that
knowledge scores improved significantly (p<0.001) over the
semester. Attitudes that assessed the role of the dietitian in the
hospital, the role of the nurse in nutrition education, nutrition
education in nursing, and general nutrition were not significantly
altered. The nursing students indicated positive attitudes towards
these parameters on the pretest, therefore there was little room for
improvement in attitudes at the posttest, which may account for the
small change seen.

16

Byrd-Bredbennor et al. (1988) determined the effect of nutrition
instruction on the nutrition knowledge, food/nutrition attitudes, and
dietary behaviors of home economics students in grades 10, 11, and
12. Students were grouped into four groups: one experimental group
was protested, taught nutrition and posttested; a second
experimental group was not protested, taught nutrition and
posttested; a control group was protested and posttested, and the
other control group was only posttested. The control groups
received nutrition instruction after the study was complete. The
nutrition knowledge test consisted of multiple-choice items related
to the instructional objectives. The Likert-type attitude instrument
assessed students' attitudes about the constructs titled 'Caring
about Nutrition', 'Eating Now Foods', 'Nutrition Affects Health', and
'Loarning about Nutrition'. A two-part food behavior assessment
form was used to assess dietary behavior. The first part was a food
frequency form with a 5-point scale ranging from 'hardly ever' to
'three or more times per day' to indicate frequency of food
consumption. The second part focused on food choices. Students
chose one of the following responses for 20 foods: a) a food I have
never eaten or heard of; b) a food I like and eat; c) a food I like but
try not to eat too often; d) a food I do not like but will eat; or e) a
food I refuse to eat. The 20 foods were grouped into five food
categories and scores were computed for the scales 'acceptance'
(student has eaten and will continue to eat the food), 'exposuro'
(student has eaten the food), 'low risk taking' (student has never
eaten the food), and 'selective avoidance' (student will avoid the
food). Results indicated that protesting did not affect posttest
scores for both control and experimental groups. The experimental
groups achieved a significantly higher (p<0.0001) mean knowledge
posttest score than the control groups. The mean attitude postscore
of the experimental groups on the attitude construct 'Nutrition
Affects Health' differed significantly (p<0.0001) from that of the
control groups, however there was no difference between groups on
the other three attitude constructs. There were also no significant
differences in dietary intake between groups. Therefore the
outcome of the study showed improvement in nutrition knowledge

17

but little change in attitude and dietary intake. The authors
suggested that many factors work against rapid, major changes in
adolescents' diets. The factors may include time demands, peer
pressure, and the fact that they do not have complete control over
their diets and the food available to them. Also because
enhancement of knowledge is a primary goal of school-based
education, teaching strategies are geared toward cognitive goals. in
the case of nutrition, such strategies are not likely to be the most
effective ones for changing attitudes and dietary behavior.

In summary of this section, the results from research on the
relationships between nutrition knowledge, attitudes, and behaviors
vary widely. Much of the conflict in the literature may be due to the
use of different methods for measuring knowledge, attitudes, and
dietary intake. Nutrition knowledge has been measured by multiple
choice tests, true-false tests, Likert-type scales, and self-
evaluation. Nutrition attitudes have been assessed by open-ended
questionnaires, Likert-type scales, personality factor
questionnaires, and still other methods. Dietary intake is measured
by a number of methods, including 24-hour recalls, food frequency
forms, one- or more-day food records, questionnaires, and food
intake patterns.

Another major problem is attitude assessment. Attitudes are
difficult to measure, and there is no one single definition of
attitudes accepted by those who do research on attitude and attitude
change. Therefore there have been many different interpretations of
attitude. Terms such as values, opinions, beliefs, motivations, and
intentions, have been used synonymously with attitude.

A third problem is that researchers test for different aspects of
nutrition knowledge and attitudes. For example, different studies
have evaluated knowledge of nutrition for the athlete, fat content of
foods, food preparation and storage, basic nutrition concepts, and
many other areas. Nutrition attitudes have been assessed by
evaluating concern or interest in nutrition, flexibility or rigidity in
personality factors, attitude toward teaching nutrition, personal
control over eating habits, foods‘ effect on health and feelings, as
well as many other aspects of nutrition attitudes. This lack of

18

consistency makes it difficult to compare across studies. Related
to this is the wide variety of population groups included in different
studies. It is difficult to generalize or compare results from
studies on athletes, adolescents, college students, middle-aged
adults, or senior citizens.

Another consideration is the validity and reliability of the
measurement instruments used to obtain the data. Some studies
report procedures used to determine the validity and/or reliability
of the test instruments used while others don't report this
information, or report that the instrument was tested and used in a
previous study. It should not be assumed that because a test is valid
and reliable for one group, it will be equally valid and reliable for
another group. In order to adequately interpret results and
determine the worth of a study it is important to know that the test
instruments used were tested for the specific purposes of the study
and found to be valid and reliable.

An overall problem in looking for a relationship between dietary
intake and knowledge and attitudes is that so many other factors
influence dietary intake besides nutrition knowledge and attitudes.
Other factors that need to be considered are social, cultural,
environmental and emotional factors, and others.

It is accepted by this researcher that there are many problems to
be solved in this type of research. Yet the need for further well-
defined research on the relationships between nutrition knowledge,
attitudes and dietary intakes was clear.

DIETARY ASSESSMENT METHODS

The scientific nutrition literature reports many ways to assess
food intake for individuals and population groups. The usefulness of
the different dietary intake measurements varies depending on the
research purpose and population groups studied. It is therefore
crucial to clearly define the research objectives before choosing a
specific method. In order to identify the most efficient and
accurate method for use in this study, three of the most frequently

19

referenced dietary intake methods were reviewed: the 24-hr recall,
food intake records, and food frequency forms.

24-Hour Recall

The 24-hour recall is a retrospective account of a subject's food
intake during the previous 24 hours. This method imposes light
respondent burden, and collection, calculation and analysis of data
can be easier than other methods (Pao et al., 1985). The personnel
time is reduced because the method requires only one interview. The
24-hr recall has been reported to provide reliable information on
mean intakes of large groups (Gersovitz et al., 1978; Karvetti and
Knuts, 1985; Linusson et al., 1974; Morgan et al., 1978; Young et al.,
1952). The 24-hr recall has been criticized because the accuracy
relies on one's memory. This method also may not capture
information that is representative of an individual's usual intake
(Pee at al., 1985).

Pao et al. (1985) examined the three-day food intake data from
the Spring 1977 Nationwide Food Consumption Survey (NFCS). One
of the research objectives using three-day diets was to determine
whether the three-day method, which combined one-day recall and
two-day food records, yielded different information for sex-age
groups from that obtained for the first day by recall. Results
indicated that mean daily intakes of calories, fat, protein and
carbohydrate for all individuals based on three-day diets were
nearly the same as those from one-day recall (coefficient of
variation within 2%). This was true for the majority of the sex-age
groups. Three-day and one-day diets yielded similar mean intakes of
four minerals (within 3%), and five vitamins (less than 5%) for most
sex-age groups. Vitamin C intakes of 15-18 year-old boys and 19-
22 year-old women differed more than 5%, and vitamin A value
differed 6-15% for 10 of the 22 sex-age groups. Authors concluded
that one-day intakes provide nearly as reliable a base as three-day
intakes for computing mean intakes of most nutrients except
vitamins A and C for large groups.

20

Fanelli and Stevenhagen (1986) assessed the consistency of the
mean nutrient and energy intakes for 2,667 non-institutionalized
persons, 65 years or older, using either 24-hr recalls or one-day
food records. The two methods gave similar estimates for the group
of older adults in this study, indicating that both methods appeared
to be equally effective for collecting group dietary intake
information.

A study conducted by Madden et al. (1976), using a similar group
of 76 elderly subjects supported the validity of the 24-hr recall
when compared to observed actual intake. Tests of the validity of
the 24-hr. recall were done by comparing actual with recalled
intakes for eight nutrients and the MAR (mean adequacy ratio).
Validity was tested by using paired Hosts and regression analysis.
In the paired t-test, no significant difference was found between
the mean recalled and the mean actual intake of nutrients, with the
exception of calories. Using regression analysis, results indicated
that for calories, protein, and vitamin A, small intakes tended to be
over-reported and large intakes under-reported. Therefore for these
three nutrients the recall seemed to be statistically conservative
for group comparisons. The conclusion was that although the
regressions for the other five nutrients seemed to support the
validity of the 24-hr. recall, further replications of this validity
study are required.

Karvetti and Knuts (1985) also tested the validity of the 24-hr
recall as compared to observed food intake of 140 men and women
15-57 years old. The observation was carried out during one day by
recording the amounts of foods selected by the subjects at four
meals. The following day, 24-hr. recalls were obtained. The
difference between mean recalled and observed nutrient intake was
between -6% and 11 %, except for sucrose (-20%) and vitamin C
(-16%). The correlation coefficient between observed and recalled
nutrient intake was in the range of 0.58 to 0.74. It was concluded
that validity was unsatisfactory at the individual level and
satisfactory at the group level.

Gersovitz et al. (1978) tested the internal validity of the 24-hr.
recall and the seven-day record among a group of 65 non-

 

21

institutionalized elderly participating in a congregate meals
program for lunch. Internal validity was assessed by comparing
reported intakes with actual intakes, obtained by weighing the foods
served. Paired t-test results for both the 24-hr. recall and the
seven-day record suggested that both methods provided about
equally accurate estimates of the mean intake. This study supported
the findings of Madden et al., suggesting that mean recalled intake
did not significantly differ from mean actual intake for the elderly
groups studied.

Gregor and Etnyre (1978) studied the validity of 24-hr recalls of
adolescent females. The actual food intake of the subjects was
known because all foods consumed were pro-weighed. The nutrient
composition of the actual meals and snacks and the reported meals
and snacks, as determined by the diet recalls, was calculated by
computerized diet analysis and by laboratory analyses. The authors
found that recalls were valid for estimating energy, protein, calcium
and zinc. However, for this group, the 24-hr recall method was not
accurate for estimating intakes of vitamins A, C, thiamin,
riboflavin, niacin, and iron.

Linusson et al. (1974) evaluated the validity of the 24-hr recall
method for 86 lactating women in a Medical Center. Validity was
tested by comparing the quantity of food consumed (by weighing)
with food recalled during an interview (24-hr. recall). Significant
differences were found between the recalled and actual amounts of
foods eaten for eight of the 14 food groups. The overall tendency to
underestimate was found to be much greater than the tendency to
overestimate. The authors concluded that much caution should be
exercised in using the 24-hr. recall method for estimating
quantitative food intake of groups. Authors noted that what is
crucial prior to determining the choice of a dietary method is a clear
definition of study objectives. If quantitative dietary information
is the need, then the appropriate method should be direct weighing
of food. However, if the purpose is to gather trends in dietary
patterns from sizeable papulation groups, then the choice of the 24-
hr. recall method is a valid one.

 

22

Young et al. (1952) conducted a study to compare a dietary
history, a seven-day record, and 24-hr recall, when estimating
nutrient intakes of individuals and mean group nutrient intakes. The
methods were compared within and between three population groups:
pregnant women, seventh and eighth grade pupils, and high school
and college students. For individuals, it was found that 24-hr recall
and seven-day records did not give the same estimates of nutrient
intake. Also the 24-hr recall and the diet history did not give the
same estimate of intake for an individual. For the groups it was
found that the estimate of mean intake obtained by diet histories
was not the same as that obtained by 24-hr recalls. However, there
were no detectable differences between estimates for the groups
obtained by the 24-hr recall and by the seven-day record. The
authors concluded that for estimates of mean dietary intake of
population groups under certain circumstances, the shorter, more
expedient 24-hr recall can be used as a substitute for the seven-day
record.

Morgan et al. (1978) assessed three methods of estimating group
and individual dietary consumption in a case-control study of diet
and breast cancer. The methods studied were a 24-hr recall, a
detailed diet history, and a four-day diet diary. Authors found a
relatively low individual correlation between the three methods,
suggesting that the 24-hr recall did not adequately predict the diet
for an individual, as compared to the four-day record. Also the 24-hr
recall and four-day record did not predict the diet as estimated by
diet histories. The authors concluded that each of the three methods
were satisfactory for estimating group values. However for
estimates of usual intake of these individuals the diet history was
more reliable than either the 24-hr recall or four-day diary.

Food Intake Record

A food record is a diary of intake recorded immediately after
consumption. Food records have been promoted because of this
direct record, and because portion sizes can be measured. But the

23

respondents' burden is increased, and subjects asked to keep a
record may subconsciously eat a better diet than usual, so the intake
information may be biased (Todd et al, 1983). Also, as more days
are included in the food record, subject compliance and accuracy
decrease. When a 7-day record was used, accuracy was reported to
decline by the fifth, sixth and seventh day (Gersovitz et al., 1978).

A study by Chalmers et al. (1952) attempted to determine how
many days a dietary record should include when dietary intakes of
groups and individuals are estimated, and which days should be
included. They also looked at how many subjects should be included
for a group study. Data were collected from regional studies of
various population groups in the northeastern United States. Included
were junior high, senior high and college students, pregnant women,
and male industrial workers. By the use of ANOVA, it was found that
for all nutrients and populations, a dietary record of one day
characterized the dietary intake of the group. It should be noted
that this answer is based on relative importance of days as
compared to number of subjects. To obtain an estimate of the mean
intake for a group with greater precision, it was more efficient to 1
take more subjects, not more days. For an individual, the number of
days to be included in the dietary record depends on the precision
required. The authors suggested use of confidence intervals, and a
logarithmic graph, and gave a specific example of how to estimate
number of days required for a given precision.

The problem of which days to use is particularly important when
dietary records are taken for one day. If a day effect should exist, it
would become necessary to select a day representative of all seven.
For the group, there were no significant differences beyond chance
occurrence between days for any of the nutrients or any of the
population types except one. The exception was the college
students, who had a distinct decrease in food intake on weekends. In
contrast to this St. Jeor et al. (1983) found no specific day-of-the-
week effect in their study of college students, faculty and their
spouses. Chalmers et al. (1952) contended that it is immaterial
which day or days one selects for a record, provided no distinct
tendency for a specified population has been found. In the case of

24

which day(s) are most representative for an individual, the authors
found no answer.

To determine the number of subjects needed in a group for a
specified precision, Chalmers and co-authors (1952) suggested the
same technique as that for determining number of days to include in
an individual dietary record. That is, a confidence interval should be
determined for the group mean, and then a logarithmic graph used to
determine the number of subjects necessary.

Stuff of al. (1983) evaluated the ability of dietary intake
collection methods to estimate nutrient intakes adequately. The
collection methods included today, 3-day, and 7-day diet records
and a food frequency form. Intakes of forty lactating women were
measured by the food frequency form and seven-day diet records.
Then one day was randomly selected from the seven-day record to
provide data for the one-day record. The 3-day record contained two
weekdays and one weekend day randomly selected from the 7-day
record. Estimates of mean nutrient intakes determined by one-day
record, three-day record and seven-day record did not differ
significantly. The one-day record showed intermediate agreement
(intraclass correlation coefficients 0.42-0.63) with the seven-day
record, and the three-day record showed good to strong agreement
(r-0.74-0.91) with the seven-day record. The food frequency form
showed poor agreement (r-0.00-0.24) with the seven-day record.
Authors concluded that the three-day record appeared to be a
reasonable approach for obtaining qualitative nutrient intake data
for population groups.

Todd et al. (1983) assessed the sources of variance in dietary
intake records. Eighteen graduate students recorded self—selected
food intakes for 30 days. The 30-day study was divided into six
periods of five days each, in which subjects alternately weighed
food intake and recorded it by tape recorder or kept a written record
of estimated food intake. Two 24-hr recalls were also obtained and
compared to the written estimates of intake. Only energy and
protein intakes were analyzed. Results showed that there was no
significant difference in the mean energy and protein intake by
recording method, i.e., taped intake records or written diary records.

25

Comparing the 24-hr recall and a one-day diary with an individual's
30-day record showed that one-day records did not accurately
represent the overall "usual” intake. Analysis of variance using
energy intake for all subjects for 30 days by day of the week
suggested that for this group the mean energy intake did not differ
during the week, especially when comparing weekday to weekend
intake. Protein intake was different, being lower on Saturday and
Sunday than on some weekdays. Given the small sample size of this
study, the day of the week effect should be interpreted cautiously
here. If people eat differently on weekends than they do on
weekdays and if an accurate assessment of intake is required, the
authors suggested that the survey should include the days of the
week that are different. The major sources of variance identified in
this study were between subject and within subject variance. This
is in agreement with a study by Beaton et al. (1979). For this study
sample, a one-day sample mean for energy was found to come within
15% of the 30-day mean energy intake of the group 95% of the time.
Increasing the number of replicate measures to five days of record
keeping increased the group mean to come within 10% of the usual
intake with 95% confidence. For an individual the one-day record
gave a very poor estimate of protein and energy intake.

Food Frequency Form

The food frequency form is another method that is used to
estimate dietary intake. This method lists representative foods by
groups and requires subjects to record their average intake (usually
by week). The food frequencies are adequate when information on
usual intakes of representative foods or food groups is desired.
However for specific nutrient assessment, food frequency is not
detailed enough. Some research on food frequency forms was
discussed under the headings of 24-hr recall and food intake record.

26

Concerns With Dietary Assessment Methods

A dietary intake pattern that has been indicated in many of these
studies (Gersovitz et al., 1978; Karvetti and Knuts, 1985; Madden et
al., 1976; Young et al., 1952) should be noted here. The ”flat-slope
syndrome” is a tendency to over-report small intakes and under-
report large intakes. When this occurs in a study, the figures will be
statistically conservative for group comparisons. The syndrome
would seldom, if ever, indicate a difference in intake where none
exists. But it could yield an indication of no significant difference
when in fact a difference does exist. Therefore the data must be
examined carefully.

A study by Guthrie and Crocetti (1985) analyzed the extent to
which nutrient intake of individuals varied over a three-day period.
It was based on a nutrient analysis of the food intake data collected
in the 1977-78 NFCS. For the three-day period a 24-hr recall of
food intake was provided by respondents, and then for the next two
days a written food intake record was kept. The authors assessed
variation within +/-15% and +l-25% for 11 different nutrients for
participants in all four seasons. The results of the analysis
indicated that there was considerable variation in the intake of
nutrients from day to day. The fact that as many as 85% of the
population had intakes of a specific nutrient on any one day that
varied by more than 25% from the average for the three-day period
confirmed that intake on any one day cannot be considered a
sensitive indicator of the usual intake of that nutrient for an
individual. Guthrie and Crocetti found the one-day records to be the
least sensitive for vitamins A and C, which was supported by Pao et
al. (1985). The authors contended that a one-day record for
representation of usual intake of an individual was inadequate and
inappropriate. The one-day record may represent the usual intake of
a group, but for nutrients such as vitamins A and C it is necessary to
have a large group.

Basiotis et al. (1987) stated that when assessing the dietary
status of an individual and groups of individuals, the day-to-day
variability in food energy and nutrient intake would affect the

27

statistical precision or accuracy of estimates of intakes. The level
of variability that can be tolerated (lo. the level of accuracy
desired) depends on the intended use of the data. The level of
precision needed may also differ by the nutrients studied.
Therefore, the authors used 29 food intake records for 365
consecutive days from a study conducted by the USDA's Beltsville
Human Nutrition Research Center, to determine the number of days
of food intake records needed to estimate ”true” average nutrient
intakes for individuals and groups with a given degree of statistical
confidence or precision. For their study, authors defined a "precise"
estimate as an X-day average intake being within 10% of the "true
average" intake for the individual or the group 95% of the time. The
true intake was the 365-day average for individuals or groups. Two
formulas were derived to calculate the number of days of food
intake records necessary to estimate true intake for an individual or
group, respectively.

Twenty-nine subjects participated in the study-13 males, age
21-49 and 16 females, age 20-53. Daily intakes of 19 nutrients
were calculated, but only food energy, iron and vitamin A were
discussed. For an individual, to estimate their food energy intake
with a 95% degree of accuracy, a range of 14 to 84 days was needed.
For iron, the number of days ranged from 18 to 142 days. To
estimate vitamin A, a range of 115 to 1724 days was needed. To
estimate the days required for a group, the males and females were
examined as separate groups. To estimate true average food energy
intake “accurately” for both groups, an intake of 3 days was needed.
For iron, the males required 7 days, and the females required 6 days.
The average number of days for vitamin A was 39 for males and 44
for females. To achieve a defined level of statistical precision for
groups, one can either increase the number of days of food intake
records for a set number of individuals, or increase the number of
individuals with a set number of food intake records. Chalmers et
al. (1952) suggested that to obtain an estimate of the mean intake
for a group with greater precision, it was more efficient to take
more subjects, not more days.

28

A study done by Beaton et al. (1979) examined sources of
variance and error in 24-hr recalls in the National Heart, Lung and
Blood Institute (NHLBI) nutrition data system. Three 24-hr dietary
recalls were collected at different points in time from sixty
subjects 25 to 44 years of age. The authors analyzed the nature and
magnitude of components of variance observed in the NHLBI nutrition
data system with regard to sex differences, subject differences
(true interindividual variation), day of the week effect, and day to
day variation in individual subjects, as well as others not relative
here.

When data for males and females were analyzed separately,
results showed that there were characteristic differences between
males and females in the amounts of foods consumed without
characteristic differences’ in the pattern of foods consumed. In the
analyses for the combined sexes, sex was a major contributor to
total variance of absolute intakes. However, when nutrient intakes
were expressed in proportion to energy intake the contribution of
sex to total variance was small. The analyses suggested that
absolute nutrient intakes should be examined separately by sex
whereas nutrient concentrations might be considered with data
pooled from both sexes.

There was a significant and consistent day of the week effect on
absolute nutrient intakes among females. This was not present
among males. The data showed that the women in this study ate
more food on Sunday than on weekdays. This is in contrast to
findings of Chalmers et al. (1952) and St. Jeor et al. (1983), who
found no significant day of the week effect. Because nutrient
concentrations were similar for all days, it might be assumed that
the patterns of food intake did not differ greatly with day of the
week. The data suggested that the mean intakes of groups,
especially females, would be significantly affected by differences
in the proportion in which weekends and weekdays are represented.
But if the prime interest is in nutrient concentrations, data might be
pooled across days without major concern.

The major components of variance identified were interindividual
variation (true between subjects variation in usual intake) and

29

intraindividual variation that included any methodologic error as
well as the true inter-day variation in intake within subjects and
the day of the week effect seen in females.

The observation of a high intraindividual variance component
implies that the precision of the estimate of an individual's usual
intake, obtained from a single one-day observation, is relatively
low. This was supported by Guthrie and Crocetti (1985) and St. Jeor
et al. (1983). The reliability of the estimate can be improved if
there is Opportunity to obtain several dietary recalls for the same
individual. To decide the required number of days of observation,
one would have to know the required precision of the estimate of the
individual‘s usual intake. If the focus is an individual, a fairly
reliable estimate of usual intake may be wanted. If the focus is the
mean intake of a group, a reliable estimate of individual intake may
be less important. The authors provided equations for individuals
and groups for calculating mean intakes with 95% confidence limits.
When the objective of a survey or study is to detect differences
between groups of individuals with confidence, the choice is
between increasing sample size or increasing the number of
replicate observations for individuals. This suggestion was also
given by Basiotis et al. (1987). Beaton et al. (1979) noted that when
considering cost, it is more preferable to increase sample size
rather than repeating dietary interviews. Chalmers et al. (1952)
also suggested that it is more efficient to take more subjects rather
than more days. Beaton et al. (1979) supported the belief that when
group size is reasonable, good estimates of group average intakes
can be obtained with one-day data.

Another concern was brought up by Guthrie in 1984. Her study
determined the amount of food that young adults select as a usual
portion size and assessed their ability to describe the food in
quantifiable terms. Students and university employees between the
ages of 18 and 30 participated in the study. Each participant was
invited individually to a buffet table, offered food items comprising
either a breakfast or lunch meal, and asked to select the amount of
food he/she considered an average serving size of each meal item.
The subjects were allowed to eat the meal, and after the meal they

30

were taken to another room where they were asked to describe in
writing the kind and amount of food they had selected. Neither food
models or measuring aids were provided.

Results showed that the amounts selected as an average portion
size deviated by more than 25% from a generally accepted serving
size in 50% to 80% of the responses for breakfast food items and in
28% to 67% for lunch items. Deviations of this magnitude have
important implications for the interpretation of food consumption
data. For example, for the people who overestimated a typical
portion size the actual intake of nutrients would be higher than that
calculated for an average portion. Actual energy intake would be
underestimated. For many items male participants chose portions
that were from 10% to 35% larger than those selected by women.
The use of the same standard portion size for men and women would
lead to errors of different magnitude for the two groups.

The analysis of the relationship between the amount of food
selected and the the amount reported showed large discrepancies.
The overall tendency was to overestimate rather than underestimate
food quantities. For seven of the twelve food items, over 25% of the,
pepple overestimated their actual serving size by more than 51%.
For nine of the twelve items over 25% of people overestimated the
actual serving size by 25%. Failure to report a selected food item
presented an additional source of error. These data reinforce the
recommendations that we need to develop and continue to evaluate
techniques to assist respondents in remembering and describing food
portions more accurately when using recalls and written food
records to assess intake. It is obvious that the concern over the
validity of reported food intake is warranted, especially when
respondents are given written instructions. An important factor in
the present study may be the students' ability to remember and
accurately describe amounts of foods.

31

METHODS FOR ASSESSING DIET QUALITY

In order to compare the quality of diets between individuals or
groups of maple a method indicating the nutritional adequacy of the
total diet needs to be established. Several methods have been used
in previous research, each with its inherent strengths and
weaknesses. This section provides a review of methods for
assessing dietary quality in order to identify an approach
appropriate to meet the objectives in this study.

Nutrients/1,000 kilocalories

The ratio of the amount of a nutrient/1,000 kcals is useful for
examining the diet quality of an individual or group. When examining
diets in terms of nutrients, it is difficult to compare persons or
groups of people because caloric intake varies widely. In general as
one increases caloric intake it is easier to meet the RDAs for
individual nutrients. Therefore one could make poor food choices and
still meet the RDAs for most nutrients by consuming a large amount
of calories. As one consumes fewer calories, choices must be of
higher nutrient density to meet the RDAs. The term 'nutrient
density' refers to choosing foods that contribute large amounts of
needed nutrients relative to the number of calories in the food.
Foods that are high in nutrients but relatively low in calories are
said to have high nutrient density, while those high in calories but
with few nutrients are said to have low nutrient density. Expressing
the nutrient composition of the diet in terms of the amount of a
nutrient/1,000 kcals allows a direct comparison of individual
nutrients between diets, thus giving an idea of nutritional quality.
This method is suitable when the objective is to examine and
compare individual nutrients and assess the quality of food choices.
The amount of a nutrient/1,000 kcals can also be used when looking
at the quality of the total diet, with focus on more than one nutrient.

32

The amount of several nutrients/1,000 kcals can be obtained and
then divided by the number of nutrients, to obtain a value indicating
the quality of the diet.

Index of Nutritional Quality (INQ)

The INQ concept provides a quantitative analysis of the diet using
nutrient standards, and also provides a profile of nutritional quality
based on a ratio of nutritive to caloric needs. The INQ was developed
from the nutrient density concept to compare the nutritive content
of a food or diet with its energy content relative to the
recommended nutritive to caloric ratio:

INC-WWW

kcal in diet per day / energy requirement
(From Sorenson et al., 1976)

The standards commonly used to calculate the INQ are the US.
Recommended Daily Allowances (USRDAs) for nutritional labeling.
However other values, such as the RDAs may be used to describe
more accurately the nutritional needs of an individual or group. An
INO value of one or greater for a given nutrient in a food or
combination of foods indicates that the food choice provides a
sufficient amount of the nutrient relative to caloric content. Values
less than one indicate that a food or combination of foods contains
excess calories relative to nutrient content. A computer program
has been developed for calculation and printing of lNOs in a tabular
and graphic form (Sorenson et al.,1976) In graphic form, the INQ
profile yields both qualitative and quantitative information. It can
be determined at a glance whether the RDAs or USRDAs were met by
observing whether the individual foods eaten additively gave an lNQ
of one or greater for each nutrient. Sorenson et al. (1976) suggested
some useful applications of the INO, such as menu and recipe
analysis, dietary evaluation, planning clinical menus to fit a

33

patients special nutritional requirements, and providing a basis for
public nutrition education.

Nutrient Adequacy Ratio (NAR) and Mean Adequacy Ratio (MAR)

The NAR is a percentage of the RDA for a single nutrient, and is
obtained by dividing a subject's daily intake of a nutrient (with
intakes greater than 100% of the RDA truncated at 100) by the RDA
for that nutrient and multiplying by 100 (Krebs-Smith and Clark,
1989). Then the MAR can be calculated by summing the NARs for
individual nutrients of interest and dividing by the number of
nutrients used. Thus MAR can be used as a measure of dietary
quality.

Nowell et al. (1985) collected 24-hr dietary recalls from 1,242
Kansas elementary school children to investigate their eating
patterns. Foods consumed were classified into categories on the
basis of similar composition, and the effect of consumption of foods
in various groups on dietary quality was determined. Regression
analysis was used to determine which food categories were
significant estimators of dietary quality, as represented by MAR
values. The MAR was calculated based on NARs for energy, protein,
vitamin A, ascorbic acid, thiamin, riboflavin, vitamin B-6, calcium,
magnesium, and iron. Results of regression analysis indicated that
the categories of milk products, vegetables, mixed protein dishes
and fruits were the best estimators of MAR values. To further
examine the relationships between dietary quality and food
consumption, frequency of foods consumed from the food categories
was determined for boys and girls according to level of MAR values,
divided into four ranges. The authors thus showed the usefulness of
the MAR method for judging dietary quality.

Krebs-Smith and Clark (1989) used MAR as a method of validating
a simplified dietary scoring system based on a food grouping
scheme. The purpose of the study was to determine the validity of
the scoring. system for measuring nutrient adequacy that could be
used by maternal and child nutrition programs. Data for the study

34

were obtained from the basic survey portion of USDA's 1977-78
Nationwide Food Consumption Survey (NFCS). Two MARs were
calculated: one assessed overall nutrient adequacy and another
represented only problem nutrients. The nutrients included in the
overall nutrient adequacy were iron, magnesium, phOSphorus,
vitamin A, thiamin, riboflavin, vitamin B-6, vitamin B-12, and
vitamin C. The authors offered no justification for selection of
those nine nutrients. Problem nutrients were those with an average
NAR below 80 for pregnant and lactating women in a subset of 274
women who participated in the USDA's NFCS. The problem nutrients
were calcium, magnesium, and vitamins A and C. Correlation/
regression analysis was used to assess the relationships between
the overall MAR and the simplified food frequency dietary score and
between the problem nutrient MAR and the dietary score. Thus the
MAR was the "gold standard" used as a measure of diet quality to
validate a simplified food frequency diet score.
Worthington-Roberts et al. (1989), studying dietary cravings and
aversions of pregnant, lactating and non-lactating women, used NAR
for computation of an “Index of Dietary Quality” (IDQ). The authors
obtained dietary information from four-day food records kept by
subjects and analyzed the records to obtain average daily intakes of
energy, protein, and 14 micronutrients. Two summary indexes were
calculated. First, mean percent RDA was computed for energy,
protein, vitamin A, vitamin D, vitamin E, vitamin C, thiamin,
riboflavin, niacin, vitamin B-6, vitamin B-12, calcium, iron,
magnesium, zinc, and folate. Then an IDQ was computed by counting
the number of nutrients for which the percent RDA consumed was at
least 66.7%. As 16 dietary components were considered, the
maximum possible IDQ was 16. Results showed that the IDQ ranged
from 8.25 to 10.09 for the first year postpartum, indicating diets of
less than ideal quality. This use of the NAR as an indicator of diet
quality is practical and sound if it is accepted that an intake greater
than 2/3 of the RDA is adequate, and anything less is inadequate.
Assigning a score of ”one“ for nutrients for which intake was
greater than 66.7% of the RDA reduces the effect of intakes greater

35

than this cutoff point pulling the mean adequacy ratio up toward
100.

The studies cited above indicate the major strengths of the MAR
and NAR. They are easy to calculate and manipulate statistically, and
can be used for validating other diet scoring methods. However,
much of the research does not offer statistical support or
justification for selection of nutrients used in calculating NARs and
MARs. If statistical procedures are not used for selecting indicator
nutrients, the chances are good that nutrients that are highly
interrelated will be used, giving a weighted score not truly
representative of diet quality. Also the MAR does not account for
nutrient density.

Correlations among nutrients have been reported in a limited
number of studies. On the basis of a correlation analysis of the
nutrient composition of 202 foods, Pennington (1976) selected seven
nutrients - vitamin B-6, magnesium, pantothenic acid, vitamin A,
folacin, iron, and calcium, as the best combination of index nutrients
for judging dietary adequacy. Pennington claimed that if a diet met
the recommended intakes for these seven index nutrients, and if a
few simple dietary guidelines were followed, there was a high
probability that all 45 essential nutrients included in her data base
would be present in the diet in adequate amounts.

Jenkins and Guthrie (1984) criticized Pennington's conclusions
because they were based on nutrient data composition of equal
portions of 202 foods without considering the relative amounts used
in a typical diet, and furthermore data on only three of Guthrie's
seven nutrients were available and complete. Jenkins and Guthrie
went on to identify a set of index nutrients that could be used for
dietary assessment. The authors analyzed 3,318 three-day food
intake records collected from the adult population surveyed in the
1977-78 NFCS. The records were analyzed using a data base
providing information for 15 nutrients - carbohydrate, protein,
calcium, phosphorus, vitamin A, thiamin, riboflavin, niacin, vitamins
B-6, B-12 and C, folacin, iron, magnesium, and zinc. A correlation
matrix of the 15 nutrients was generated and then a factor analysis
with an orthogonal varimax rotation was performed. Four factors

 

 

36

were identified and then index nutrients for each factor were
determined. The four index nutrients identified by Jenkins and
Guthrie as reflecting adequate intakes of the other 11 nutrients
were vitamin B-6, iron, calcium, and vitamin A. These nutrients
need to be tested under other circumstances and populations to see
if they hold as adequate index nutrients.

Food Groups Methods

Food groups are sometimes used as a scoring method for
assessing dietary adequacy. Numerical scores have been generated
to reflect intake from the four food groups, or other food grouping
schemes. Examples are EFNEP's Twenty-four Hour Diet Score and a
simplification of EFNEP's system by Guthrie and Scheer (1981).
Because the nutritional intakes in the present study were analyzed
by nutrient content rather than food groups, the food groups method
was not considered apprOpriate for assessing dietary adequacy in
this study.

To conclude, there are many approaches that can be taken in
assessing nutritional adequacy, and none of those methods are ideal.
Because the computerized diet analysis used in the present study
was based on nutrient intake and the percent of the RDA consumed,
the MAR seems to be the method best suited to analysis of the data
gathered in this study.

VARIABLES RELATED TO DIETARY INTAKE

It is well known that dietary habits are difficult to change
because eating is related to many factors. Researchers have
identified some of these factors, such as gender (Haseba and Brown,
1968; Beerman et al., 1990), body weight (Wakefield and Miller,
1971), length of time in college (Brown, 1967), subscription to a
meal plan (O‘Leary and Lee, 1975; Gottschalk et al., 1977; Jakobovits

 

 

 

37

ot al., 1977), patterns established prior to attending college (Stasch
et al., 1970), place of residence while in college (Beerman et al.,
1990; O'Leary and Lee, 1975), education (Young et al., 1956), the
symbolic meaning attached to food (Pumpian-Mindlin, 1954),
economic factors (Wilhelmy et al., 1950), other family members
(Dickens, 1965), advertising (Holden, 1971) personal needs
(Lowenburg et al., 1974), and the family life cycle (Coughenour,
1972).

Of the above mentioned factors, gender and place of residence
were two of the available factors in this study that were evaluated
for their ability to predict diet quality. O'Leary and Lee (1975)
compared the nutrient intakes of female students living in
university residence (n=75) to the intakes of female students living
at home (n=32). Seven-day food records were used for the collection
of dietary information. Results showed that mean daily intakes of
calcium, riboflavin, and thiamin were significantly higher (p<0.01)
for students living in residence halls than for students living at
home. Mean daily vitamin A intake of students in residence halls
was also significantly higher (p<0.05). However it was found that .
for both groups of students mean daily intakes of all nutrients were
similar to or in excess of the Canadian Dietary Standards.

Beerman et al. (1990) identified differences in food choices
among students at Washington State University living in on-campus
(n=65), off-campus (n=62), and Greek housing (n=25). The authors
also identified gender differences in food choices. The students
completed a food frequency for evaluation of food choices. The
authors found that students living on campus ate more fresh fruits
and vegetables, drank less beer, and consumed more cookies and fish
than students living elsewhere. On-campus students were less
likely to select white bread and unsweetened cereals. Significant
differences were found between men and women for some of the
food selections. More women drank sugar-free soft drinks (p<0.05).
Men were significantly more likely to consume beer (p<0.01) and had
greater meat consumption (including chicken, beef, pork, and fish:
p<0.05). The men consumed more white bread (p<0.05) compared
with women, who preferred whole grain breads. Though it was not

38

an objective of this study to examine the factors that influence
dietary intake, the available demographic and environmental factors
(age, gender, place of residence, class level, and body mass index)
were evaluated for their ability to predict dietary quality.

COMPUTERS IN EDUCATION

Computers have been used to assist in education for many years.
The initial efforts incorporated the first generation of computer
hardware, which was large, expensive and relatively unreliable by
today's standards. But since their introduction, computers have
decreased greatly in size and price, and have increased in capability
and reliability. With the relatively inexpensive and popular
microcomputers available now, computer-based education is
affordable to most educational and training institutions. There has
been much research on effectiveness and uses of computers in
education. Some of the research is summarized in this section, to
provide rationale for the decision to use a computerized program in
this research study.

Kulik et al. (1980) conducted a meta-analysis to integrate
findings from 59 independent evaluations of computer-based college
teaching between 1967-1978. The studies used in the meta-
analysis described four major types of applications of the computer
to instruction, in varying degrees of use: tutoring, computer-
managed teaching, simulation, and problem-solving. The meta-
analysis gave equal weight to each independent study, regardless of
number of students, classes, or comparisons described. The overall
conclusion was that the computer has made a small but significant
contribution to the effectiveness of college teaching. In a typical
implementation, computer-based instruction (CBI) raised
examination scores by about three percentage points, or about one-
quarter standard deviation. Computer-based teaching produced
small positive effects on attitudes of college students toward
instruction and toward the subject matter taught.

39

Dance (1980) also reviewed research on computer-assisted
instruction (CAI) between the 1960‘s and 1970's. The studies she
summarized identified effectiveness of CAI under certain
conditions. Two areas in which CAI programs were consistently
proven effective were foreign languages and the sciences (Koch,
1973; Allen, 1972; Suppes and Morningstar, 1969; Magidson, 1978;
Bitzer and Alpert, 1970). Also, two separate studies (Lewellen,
1971; Vinsonhaler and Bass, 1972) found that CAI students
performed better on standardized tests than students being provided
traditional instruction. Another advantage of CAI was that it
allowed students to progress at their own rates (Okey and Mayer,
1976), and it permitted repeated review of material (Grimm, 1978;
Magidson, 1977). Prompt feedback also helped to make CAI
effective. One study cited (Kulhavy, 1976) found that students
remembered more from a lesson when they were given immediate
feedback, and this was supported by other studies (Grimm, 1978;
Tait et al., 1974). In one survey of CAI studies (Magidson, 1978), the
author found CAI to be at least as effective as traditional
instruction in 55% of studies, and more effective in 45%. Some
studies (Lewellen, 1971; Allen, 1972; Molnar, 1972; Bitzer and
Alpert, 1970) found that CAI students took less time to learn as
much or more material than students under traditional instruction.
The author concluded that much more research is needed to clarify
the role of CAI in instruction, and CAI can then be used in those
situations where it will enhance learning for individuals or groups.

A study of CAI in nursing education was conducted by Conklin
(1983). The author sought to determine if CAI as a teaching
technique would improve the achievement scores and attitude
toward CAI in a third year nursing course, and if the improvement
would be greater than that shown by students assigned reference-
reading only, and students given no instruction. The CAI package
used was developed specifically for the 'Nursing of Adults‘ course.
The specific purpose was to augment surgical nursing knowledge
gained through traditional instruction. The students were divided
into three groups: 1)"CAI" group- a group taking the course, and
participating in a six-week surgical nursing practicum and exposed

40

to the CAI component; 2)"Traditional group”- students in the course
and also doing the six-week surgical practicum, but assigned
reference readings; and 3)” Control group"- from a different section
of the class who did not have surgical nursing instruction or
practicum. Students were given pre- and post-tests, consisting of a
multiple-choice achievement test, and a semantic differential
questionnaire to assess attitudes toward CAI. Results revealed that
the CAI group improved significantly (p<0.001) on achievement
scores, and the improvement was significantly greater than that for
traditional and control groups (p<0.001). Mean scores of the attitude
test improved from pro-to post-test in all three groups, so no
difference was found between the CAI group and the other two
groups in change of attitude toward CAI. Results from this study,
combined with literature cited by the author from other areas of the
health profession promoted the general consensus that CAI can be
used effectively in such settings as nursing education, continuing
education, inservice education, community health, hospital
orientations, and nursing refresher courses.

Schroeder and Kent (1982) carried out a study to determine
whether a computer-based instructional system for teaching renal
diet therapy could teach as well as, or better than traditional
instruction, and to compare students' attitudes toward the computer
teaching method and traditional method. A CBI program was
designed specifically for this purpose, and provided tutorial and
drill-and-practice sessions. Junior and senior dietetics students
were divided into two groups, one group to be self-instructed by
computer, the other to be taught by a traditional lecture/laboratory
mode. Multiple-choice pro-and post-tests for knowledge were
administered, and following the post-test a Likert-type instrument
measured attitude toward the respective teaching method for each
group. Analysis of pro-test and post-test scores showed no
significant difference in achievement between the two instructional
groups. The students who learned by computer had more favorable
attitudes than the conventionally taught students. Therefore the
study supported the use of a CBI system as a primary means of
teaching renal diet therapy to dietetics students.

 

41

Deardorff (1986) compared a computerized format to written and
face-to-face formats for presentation of medical information to
undergraduate students in introductory psychology courses.
Participants were randomly divided into four groups and received
information by written presentation, a computerized presentation, a
face-to-face presentation, or a control group who received no
information. Subjects in the control group completed a general
information sheet and then were given magazines to peruse while
the other groups were receiving their respective presentations. The
information presented to the groups was on sexually transmitted
diseases. At the end of the presentations, participants were
administered four evaluations. These consisted of an assessment of
state or situational anxiety, a semantic differential questionnaire
assessing the information delivery format, a free recall task which
asked participants to write all the information they could remember
from the presentation, and a cued recall task consisting of a 20-
item multiple-choice test. Results for the state of anxiety scores
revealed no significant difference between delivery formats. The
semantic differential responses indicated that generally the .
computer and face-to-face formats were equal, and both were rated
higher than the written format. The free recall and cued recall
(multiple-choice test) both showed that computerized and written
formats did not differ in proportion recalled, and both of these were
superior to the face-to-face format. The results supported the
conclusion that a computerized health education format is just as
effective as a written format. Also, the computerized format was
rated positively, as was the face-to-face method.

Rios and Granell (1985) developed a college-level CAI lesson on
vegetarianism, and compared its effectiveness with the lecture/
discussion method. Forty-four students enrolled in an introductory-
level foods and nutrition course comprised three study groups: CAI,
lecture/discussion, and a control group. All three groups completed
a pro-test, and one week later the CAI and lecture/ discussion
groups received their respective lessons. The control group received
a lesson on another topic. Three weeks after the lesson, a post-test
was completed. Results showed no significant effects on learning

 

 

42

with either CAI or lecture/discussion method. Authors noted that
due to the small sample size and specificity of subject matter, the
results should be interpreted cautiously. However, the findings did
support previous research in suggesting that CAI is a feasible
alternative to the lecture/discussion method in the college nutrition
classroom. These findings were also supported by Wade and Thiele
(1973), who compared a CAI group to a non-CAI group in a college-
level Food Science and Nutrition course, and found CAI to be as
effective as a non-computer assisted program.

Carew et al. (1984) developed a CAI program to serve as a study
guide in a college-level introductory nutrition course. Students used
the program on a volunteer basis to review or clarify lecture notes,
to review difficult concepts, or to help prepare for exams. Students
using the CAI program were given a questionnaire at the end of the
semester. This was not a controlled study, but preliminary data
provided some indications about the use of CAI. Most students (51%)
thought the CAI approach was useful, and 52% felt it should be
required rather than optional. The most important question asked
was whether the students thought using CAI improved their grades.
Before grades were distributed, 83% of students thought using the
CAI definitely had improved their grades. After analyzing grades, it
was found that the mean course grade for users was 81.3, while that
of non-users was 76.2. This difference in grades may be because the
CAI users were more motivated or better students to begin with, not
that using the computer improved their scores significantly.
Regardless of this, the students enjoyed using the program, and the
feasibility of CAI as an alternative in classroom education was
again supported.

A study designed to compare the effectiveness of CAI and lecture
method of instruction (LMI) for teaching sanitation to hospital
foodservice personnel was carried out by Waddell et al. (1985). Two
independent variables, gain in sanitation knowledge, and amount of
time required to complete the training experience were compared
between CAI and LMI groups and a control group. The effects of
employees' ago and education level were also investigated. A
sanitation knowledge test, in a pro-test and post-test format, was

43

used to measure gain in knowledge, along with an attitude
assessment questionnaire to assess attitudes toward sanitation
training and CAI. The control group was administered the pro-and
post-tests and received no training. 230 foodservice employees
participated in the training. The CAI training consisted of three
diskettes, used at individually scheduled times. The LMI group
attended one lecture session. The CAI and LMI group attained
significantly higher scores (p<0.001) than the control group. The CAI
group obtained higher gain scores than the LMI group, but the
difference was not significant. The age and levels of education did
not affect the gain scores. In regard to time required to complete
the training, the LMI group required one-third the time of the CAI
group, contrary to the researchers' expectations. Possible
explanations offered for this were that the participants were not
limited to the time they could spend on the computer, and because of
the novelty of the situation, workers might have intentionally
repeated parts of the lesson to spend less time at work. Date from
the attitude assessment questionnaire showed significant
differences (p<0.001) in scores from pro-test to post-test in the ‘
CAI group, indicating that attitudes toward CAI improved as a result
of participation. To conclude, the CAI was as effective as the LMI in
increasing knowledge, but the authors emphasized that further
research is needed to determine whether CAI is a cost-effective
method for training hospital foodservice personnel.

In discussing computers and health education, Gold and Duncan
(1980) stated that one of the most basic ways that computers may
serve as motivational devices is through the use of self-assessment
programs. One such approach is computerized dietary analysis
programs. These programs allow students to input information on
what they eat, and receive immediate output about how well they
met their dietary standards. Authors stated that such programs can
provide an excellent introduction to the study of nutrition by
developing and focusing student interest.

Weinberg and Scott (1983) also discussed the use of the
computer in nutrition education. They focused on using advances in
technology to efficiently translate knowledge of nutrition to the

44

public. Patient education is one place where technology can be taken
advantage of. Computerized programs allow for the patient's active
participation in the education process. Computers are also
advantageous in that they are available 24 hours a day. When used
for nutrient analysis, computers provide personalized advice and are
a valuable adjunct to one-to-one counseling. Self-monitoring, which
is a strong behavioral change technique, is facilitated by the
computer. The authors concluded that the potential for

computers in health promotion is great, as the computer can handle
multiple inputs and provide multiple outputs, and cater to an
individual's specific needs and demands. But further research must
demonstrate effectiveness and cost-effectiveness of computer

apphcaflons.

CHAPTER THREE

MED-DDS

Subjects

Prior to collecting data, an approval for the study was obtained
from the University Committee on Research Involving Human
Subjects (Appendix A). A consent form was developed for the
students who agreed to have their data used for research purposes
(Appendix 3). Students in the college-level introductory nutrition
course (two live lecture sections) at Michigan State University
comprised the control and experimental groups. The course
enrollment for the fall 1988 quarter in the two sections was 711
students. Two sections of the course were offered consecutively, by
the same instructor in the late morning, three days a week. This
helped minimize student bias between the two sections.

Of the students in the course, approximately 47% were freshmen,
36% were sophomores, 11% were juniors, and 5% were seniors.
Students from all majors enrolled in the class in part because the
course fulfills a Natural Science general education requirement at
MSU. There were no differences in the students' demographic
backgrounds (class level, college major) between the two sections.

Instruments

The test instruments included a multiple-choice nutrition
knowledge test and a Likert-type nutrition-related attitude
questionnaire (Appendix C). To estimate students' dietary behavior,
food intake data were collected from one-day dietary records

(Appendix D).

45

46

Nutrition Knowledge Test

The 22-question knowledge test was formulated from test
questions used in examinations during previous terms of the class.
Difficulty and discrimination indices from five previous
examinations were used in selecting appropriate questions.
Difficulty scores (percentage of students marking the wrong
answer) of the knowledge test questions ranged from 8 (easy) to 49
(difficult). In order to cover the depth of information presented in
the class, questions were selected to range from easy to difficult
and to cover general as well as detailed information. Discrimination
scores (difference between tap 27% and bottom 27% of students
marking the correct answer) ranged from 0.33 to 0.68. These are
commonly accepted cutoff points for discrimination scores (Cracker
and Algina, 1986). The formulated questions were tested for face
validity by faculty associated with the course and by graduate
students in the Department of Food Science and Human Nutrition
during the 1988 winter quarter.

Nutrition Attitudes Test

Copies of attitude instruments were obtained after the literature
was reviewed, relevant studies were identified, and authors were
contacted. The attitude instrument for this study was compiled
based on research from two previous publications (Boron et al.,
1983; Christopher at al., 1980). The two instruments selected had
been formulated specifically for college students enrolled in
introductory nutrition courses. These instruments included two
constructs related to this study; flexibility or rigidity in attitudes
toward nutrition and concern about nutrition.

Previous literature (Carruth et al., 1977; Jalso et al., 1965) has
suggested that flexibility or rigidity in attitudes and personality is
an important factor in determining a person's willingness to change.
The attitude instrument from Boron et al. (1983) measured the
construct of flexibility/rigidity in attitudes toward nutrition.

47

Attitude statements were written in a Likert format with a five-
category continuum from ”strongly agree " to "strongly disagree”.
Negatively worded items were reversed for scoring. Thus, the higher
the total score was, the less flexible the attitude toward nutrition
was considered to be. Content validity was established by the
authors on the basis of information provided by experts in nutrition,
education or instrument development. Reliability was determined by
split-half reliability (Spearman Brown Pr0phecy Formula) and
Cronbach's Alpha reliability. Split-half reliability was reported as
0.86 for the Boron et al. study, and Cronbach's Alpha was reported as
0.84.

The attitude instrument used by Christopher et al. (1980) was a
compilation of items selected from various other instruments and
these items also were written in a five-point Likert-scale format.
Using factor analysis, the authors had identified five factors within
their attitude instrument: ‘Concern about Nutrition', 'Breast Feeding
is lmportant‘, 'Nutrition is Important in Pregnancy', 'World Food
issues', and 'Attitude about Televised lnstruction'. Because it has
been suggested that a person's concern about nutrition will affect
his/her motivation to change (Eppright et al., 1970; Grotkowski and
Sims, 1978), the 'Concern about Nutrition' factor was selected for
use in the present study. Christopher et al. (1980) reported the
reliability coefficient (Cronbach's Alpha) for this factor as 0.91 and
0.88 from testing at two different campus branches.

Although the attitude measurement instruments had been tested
by the original authors (Boron et al., 1983; Christopher et al., 1980),
they were tested again by this researcher in the spring quarter
(1988) to confirm the reliability for the HNF 102 class at MSU. The
attitude instruments were pilot tested at one time point, rather
than a test/retest, with this HNF 102 class, therefore changes in
attitude were not tested. In testing the construct of
flexibility/rigidity (Boron et al.,1983) the Likert scale was
reversed, reading from ”strongly disagree” to ”strongly agree" so
that a high score would indicate a more flexible attitude toward
nutrition. Negatively worded items were reversed for scoring. Thus
the higher the total score, the more flexible the attitude toward

48

nutrition was considered to be. Using the Statistical Package for
Social Sciences (SPSSX) (SPSS Inc., 1988), a reliability analysis was
computed. By examining the correlation matrix, five of the items
were determined to have low correlations (r-0.0-0.3) with the other
items. Coefficient Alpha, which measures how well the items are
working together as a scale, was increased by excluding those five
items. The final nine-item scale reliability (Coefficient Alpha) was
0.81.

The 'Concern about Nutrition' scale of Christopher at al. (1980)
also was tested with the MSU HNF 102 students. Again using SPSSX,
a reliability analysis was computed. Three items were deleted
because they had low correlations (r-0.0-0.3) with other items in
the correlation matrix, and Coefficient Alpha was increased by
excluding them. The final fourteen-item scale reliability
(Coefficient Alpha) was 0.92. Both constructs ('flexibility/rigidity
of attitudes toward nutrition' and 'concern about nutrition') were
combined to make a 23-item attitude instrument for use in this
study, as they were both considered important.

Dietary Intake

Dietary intake information was collected using a one-day dietary
intake form (Appendix D). The one-day diet record was chosen
because recording and analyzing the data can be easier than other
methods or using more days (Pao et al., 1985), and it was assumed
students would more readily participate. Also previous literature
has supported the ability of one-day dietary intake records to
represent group mean dietary intakes. All students were instructed
to record their complete intake for one weekday, including all foods
and beverages, condiments, method of preparation, serving sizes,
etc. Students in the control group turned in their food records and
also recorded gender, age, height, weight, and activity level
information so that the computerized diet analysis program could be
run by the researcher. Students in the experimental group were
given complete instructions on how to run the computerized diet

49

analysis program and were instructed to turn in the printout of their
diet analysis.

The diet analysis program used, 'MSU NutriGuide' was developed
in the nutrition department at Michigan State University
(Department of Food Science and Human Nutrition, MSU, East Lansing,
MI). It was initially developed specifically for use by the students
in HNF 102 and contains a unique educational component designed to
reinforce the information learned in the class. The program was
designed to assess dietary intake over a 24-hour period and analyze
these foods for 27 nutrients. The nutrient intake was then compared
to the 1980 RDAs and other dietary guidelines. In addition, the
program calculated daily caloric needs based on age, sex, body size
and activity level. The database consisted of 730 foods, including
foods selected from the MSU Nutrient Database and food items from
the MSU residence hall food service. Recipe analysis information for
residence hall items was obtained from MSU Food Service and added
to the MSU NutriGuide Database. The educational component includes
information on the 27 nutrients assessed within the program and
information on current nutrition issues, some of which are covered

in the course.

Procedures

Both sections of the nutrition course covered the same lecture
material and were taught by the same instructor. The teaching
method was consistent for both sections, and the same examinations

were administered on the same days.

Data Collection

The control and experimental groups were administered pre- and
post-tests on nutrition-related knowledge and attitudes. The pre-
and post-tests were not required as part of the course. Because the
research study was independent of the course, the instructor and

50

researcher agreed that the pretest and posttest be completed on a
voluntary basis, rather than as a course requirement. The protest
was given the first week of class, and the posttest given the last
week of the 10-week quarter. In addition to knowledge and attitude
questions on the posttest, students in the experimental group were
asked questions related to the computerized diet analysis
assignments. Students recorded responses on computer-readable
scoring sheets. The scoring sheets were sent to the computer center
scoring office and item analysis run on the knowledge test
questions. Data analyzed by the scoring office are recorded on
magnetic tapes. The knowledge and attitude responses were
transferred to floppy diskettes to be available for statistical
analysis. Data for each student were identified by student number
only.

To assess dietary intake, both sections of the nutrition course
received the same diet record assignments. During the first week of
the quarter and the second to last week of the quarter, the control
and experimental groups completed one-day diet intake records. The
control group handed in the records and received no feedback until .
the end of the quarter. The experimental group used MSU NutriGuide
to analyze their diet records and to receive immediate feedback both
times. The experimental group was encouraged to assess their diets
by using the program as frequently as desired during the term. The
diet records from the control group were analyzed with the same
computerized program by the researcher so that the dietary intake
data could be compared between the two groups. The students
received four points (on a 100 point scale) for completing the two
diet record assignments.

All dietary intake data were automatically stored on floppy
diskettes, and after each assignment was turned in, data were
downloaded onto a master diskette. The dietary intake information
was also identified by student number. At the end of the quarter,
the knowledge and attitude responses were merged with the dietary
intake information by matching student numbers. Data for students
who completed both the knowledge and attitude pre- and posttests

51

and the pre and post diet record assignments were retained for
research purposes.

Statistical Procedures

For data analysis the Statistical Package for the Social Sciences
(SPSS-X) program was used.

Means and standard errors were obtained for the scores of the
knowledge test, attitude questionnaire, and the nutrient intake data
estimated by the dietary analysis program.

ANOVA procedures were used to determine whether there was a
significant treatment effect (experimental vs. control group), time
effect (pretest vs. posttest), or an interaction effect (treatment by
time) for knowledge, attitude and dietary data. For this study,
MANOVA (multiple analysis of variance) was not chosen as a method
of statistical analysis for the dietary data. MANOVA is classically
used when there are multiple dependent variables, as with the 27
nutrients included in the dietary analysis. However, in order to .
adequately interpret the output generated by MANOVA a hierarchy of
dependent variables needs to be established. Because our knowledge
of nutrients at this point is inadequate to identify nutrients from
most to least important, it was decided that the MANOVA procedure
was inappropriate.

Where significant differences were determined by ANOVA, t-
tests were used to compare the two groups and two time points in
this study. When a significant treatment (group) effect was found,
the independent samples t-test was used to evaluate whether the
difference between the groups was at the pretest and/or posttest,
for knowledge, attitude and dietary data. When a significant time
effect was detected by ANOVA, the paired samples t-test was used
to determine whether the difference was within the control and/or
experimental group for knowledge, attitude and dietary data.

Dietary data were evaluated for the entire study population and also
for females and males separately.

52

Measurement of Dietary Quality

Nutrients included in the calculation of the Mean Adequacy Ratio
(MAR) score were protein, vitamins A, D, E, B-1, B-2, B-6, B-12 and
C, niacin, folacin, iron, calcium, phosphorus, magnesium, and zinc.
These nutrients were selected because they have established RDAs,
and were included in the dietary assessment program. However, the
database contains incomplete data for zinc. Because of the
incomplete data for zinc, the MAR scores also were calculated
without zinc. There were no differences in the MAR scores with or
without zinc, so zinc was left in the calculations. For the other
fifteen nutrients the database is approximately 90% complete. To
calculate the MAR, first the Nutrient Adequacy Ratio (NAR) is
determined for each nutrient. The NAR is each persons' percentage
of intake of the RDA for each nutrient. All NARs with a value
greater than 100 were truncated to 100 to prevent high intakes of
some nutrients from pulling the mean score up. Then all NARs were
summed and divided by the total number of nutrients included to
obtain the MAR.

Variables Related to Nutritional Adequacy

Multiple regression analysis was used to examine the
associations between available demographic and environmental
variables (independent variables) and nutritional adequacy
(dependent variable). Independent variables included age, gender,
place of residence, class level, and body mass index (BMI). These
variables were selected because of data availability. The dependent
variable was nutritional adequacy as indicated by MAR. In this study
the backwards method of regression analysis was used. In this
method first all the independent variables were entered into the
regression equation. A t-value was determined for each variable,
and the significance of the t was determined. The variable with the
least significant t-value (greater than 0.05) was deleted from the
equation and the equation was recalculated, with new t-values

53

determined for each variable. The process was repeated with
variables deleted one by one until only variables .with significant t-
values (p<0.05) were remaining. Because the regression coefficients
were each determined by a different scale (each variable as it
related to MAR) the coefficients could not be compared to determine
their relative importance in the equation. In order to compare the
coefficients they were transformed to the standardized z-scale, and
are termed beta-weights. Thus the beta-weights can be compared to
determine the relative importance of each variable in the equation.
When the prediction of the dependent variable is based on two or
more independent variables an adjusted multiple correlation
coefficient (R) is computed. The adjusted value is the correction for
bias resulting from procedures used in determining the multiple
regression equation. The squared value of the adjusted multiple
correlation coefficient (R2) describes the proportion of variance in
the dependent variable predicted by the regression equation. In the
actual computation process, dummy variables were substituted for
class levels (i.e., 1 representing freshmen, 2 representing
sophomores, 3 representing juniors, 4 representing seniors, and 5
representing any other level), gender (i.e., 1 for female and 2 for
male), and place of residence (i.e., 1 being residence hall, 2 being
off-campus housing, 3 being fraternity/sorority, 4 being at home
with parents, and 5 being any other living situation). For age and
BMI, actual values were used.

Finally, Pearson product-moment correlation coefficients were
computed to determine the relationship between nutrition knowledge
and attitudes, nutrition knowledge and MAR, and nutrition attitudes
and MAR. Because there were no significant differences between the
control and experimental groups in terms of knowledge, attitudes,
and dietary data, group data were combined for this statistical
procedure.

CHAPTER FOUR

RESULTS AND DISCUSSION

SUBJECTS

The subjects included in the data analysis were 254 students who
completed both knowledge and attitude pro-and posttests and pre
and post diet record assignments. The study sample comprised 36%
of the total class population: 49 percent of the students in the
control section (n-177) and 22 percent of the students in the
experimental section (n=77). This author suggests that the
difference in participation rates was due to the difference in the
workload for the diet record assignments. The control group
recorded one-day intakes and turned them in. The researacher
analyzed the diet records and gave feedback to the students at the
end of the term. The experimental group recorded one-day intakes
and self-evaluated their diets using the computerized diet analysis
program and then turned in their printouts. The diet analysis
program generally took twenty to thirty minutes to run. The
assignment was given twice during the quarter, and therefore
involved more work for the students in the experimental group.

Another possible explanation for the seemingly low participation
rates for the experimental group lies in the method of data
collection for the computerized diet analysis program. After the
students entered their dietary intake data the nutritional
assessment was printed. Data storage was carried out after the
printout function. From information obtained from the computer lab
monitors it is possible that many of the students simply did not
have their dietary data stored because they stepped at the printout
step. There is no way of determining how many students did not
save their dietary data.

The response rate for the protest in the control group was 82%,
and 83% for the experimental group. Sixty-eight percent of the
students in the control group completed the posttest with 64% in the

54

55

experimental group. Eighty-three percent of the students in the
control group completed the first diet record assignment, while 92%
in the experimental group completed the assignment. For the post
diet record assignment 68% of the students in the control group
returned it while 66% of the students in the experimental group
completed theirs. Therefore, the response rates for each individual
piece were fairly good. However all four pieces were necessary for
data analysis, and many students were likely missing only one piece.

It was determined that the study group was a representative
sample of the class population based on the class level and place of
residence (Tables 1 and 2).

Table 1. Class level of students

 

 

Total class Study sample
n-711 n-254
% %
Freshman 4 7 3 6
Sophomore 3 7 3 9
Junior 1 1 16
Senior 5 7
Other 2

 

 

Table 2. Place of residence of students

 

Total ficlass Study sample
n-711 "-254

% %

Residence hall 7 3 7 0
Off-cam pus

housing 21 2 2

Fraternity/Sorority 2 2

Home with parents 2 4

Other 2 2

 

56

The majority of students that enrolled in the introductory
nutrition course were freshmen and sophomores (Table 1). Because
most of the students were underclassmen, the majority lived in the
residence halls (Table 2). Place of residence would likely affect
their dietary intakes, due to residence hall food service (Beerman et
al., 1990; Gottschalk et al., 1977; Jakobovits et al., 1977; O'Leary
and Lee, 1975).

Data are not available for the gender of the students in the entire
class. For the study population information on males and females is
presented in Table 3.

Table 3. Percent of males and females in the study group

 

 

Females Males
% %
Control (n-177) 8 0 2 0
Experimental (n-77) 7 8 2 2
Study population (n-254) 7 9 2 1

 

NUTRITION KNOWLEDGE

The knowledge test score (1 standard error) increased from 7.9 i
0.2 in the pretest to 12.8 i 0.3 in the posttest (maximum possible
score of 22) in the control group. In the experimental group, the
average pretest score of 7.4 1 0.3 increased to 14.9 .+_ 1.6 in the
posttest (Table 4).

57

Table 4. Knowledge test scores

 

 

gm __Pretest* fosttest‘

X SE X SE
Control (n=177) 7.9 0.2 12.8 0.3
Experimental (n-TD 7.4 0.3 14.9 1 6

 

" Maximum possible score-22

Table 5. ANOVA for knowledge test means‘

 

 

 

Effect F Significance of F
Time ' 119.24 <0.001
Group by time 5.35 <0.05
1"II-254

Results from ANOVA indicated significant time and interaction
effects (Table 5). The increase in scores from pretest to posttest
(time effect) was significant (p<0.001) for both control and
experimental groups, as determined by paired t-tests. This was an
anticipated outcome of the course because enhancement of
knowledge is a primary goal of education. The increase in nutrition
knowledge resulting from the course has been supported by other
studies where a pretest-posttest design was used (Brush et al.,
1986; Byrd-Bredbenner et al., 1988; Carruth et al., 1977; Rosander
and Sims, 1981; Ross, 1984). However, there was no significant
difference between the knowledge scores of the groups at the
posttest (beta-0.01). Therefore, as far as the first objective of the
study, it must be concluded that use of the computerized diet
analysis program as used in this course, did not significantly
enhance nutrition knowledge beyond the improvement resulting from
the course.

58

NUTRITION ATTITU DES

The attitude pre- and posttest score means (3; SE) of the control
group were 23 :I: 0.7 and 22 1 0.9, respectively (maximum possible
score - 46). The attitude pre- and posttest score means of the
experimental group were 22 1, 1.4 and 23 1 1.1 respectively (Table
6). Results of ANOVA showed no significant group, time, or
interaction effect.

Table 6. Attitude test scores

 

 

Group _Pretest* __Posttest*

x SE X SE
Control (n-177) 23 0.7 22 0.9
Experimental jn-77L 22 1 4 23 1 1

 

*Maximum possible score-46

In other studies where changes in nutrition attitudes were
measured by pretest and posttest, results have varied ( Brush et al.,
1986; Byrd-Bredbenner et al., 1988; Ross, 1984). In the study by
Brush et al. (1986), no differences were found in nutrition attitudes
of adults 19 to 65 years old from pretest to posttest. One
explanation offered by the authors for the lack of change was the
possibility of a ”ceiling effect”. At the pretest both the treatment
and control groups scored over 82% of the maximum possible score
for the nutrition attitudes scale. Therefore the attitudes of their
study participants may have reached their ”ceiling”. However, this
was not the case in the present study. Both control and treatment
groups scored around 50% of the maximum possible score on the
attitude scale at the pretest, therefore the ceiling effect cannot be
considered as an explanation for the lack of change in attitudes.

Byrd-Bredbenner et al. (1988) also found little detectable change
in attitude scores from pretest to posttest in their high school home

59

economics students. The authors suggested that because school-
based education is geared toward cognitive goals, the curricula and
teaching strategies are likely not the most effective ones for
changing nutrition attitudes. General nutrition curricula, such as
the one used in their study, helped students comprehend basic
nutrition principles rather than change specific dietary attitudes
and behaviors. This is also a plausible explanation for the results
found in the present study, because of the general nature of the
basic nutrition course.

In the study by Ross (1984), nutrition attitudes of college-level
nursing students did not change from pretest to posttest. Ross
found a decrease in score (less positive attitude) for two attitude
statements at the posttest. She suggested that means of pre- and
posttest series may regress to the mean when completed over short
periods, and this may account for the small amount of change soon in
her study.

The finding from this study of no changes in attitudes could be
attributed to several reasons. Attitude change was not a specific
goal of the curriculum, however it was likely an intended goal along I
with knowledge change. If some attitude change was stimulated, the
9-week interval between pre- and posttest may have been
insufficient to demonstrate any attitude change. It is also possible
that the attitude instrument was not sensitive enough to pick up
changes in attitudes if there were any. It is possible there may have
been changes in attitudes other than those assessed in the nutrition
attitude questionnaire. Perhaps if “satisfaction with the course” or
"ability to apply the information taught”, or some other attitude
constructs were assessed, a difference could be determined between
the group that used the computerized diet analysis program and the
control group. Determining which specific attitudes will be
addressed in an educational program is difficult because the effect
of the program on attitudes may vary from person to person. The
actual attitude changes, if any, may be intended or unintended. Also,
attitudes may be relatively resistant to change (Swanson, 1972).
Finding a valid and specific measure of intended attitude change is
difficult.

60

Another problem with attitude measurement occurs when the
subjects know that they are part of a research project. When
subjects are aware that their data are being analyzed, they are
likely to modify responses because of wanting to please the
researcher, wanting to give a good impression, giving responses that
they believe are socially ”acceptable”, etc. (Fishbein and Ajzen,
1972). However, because the response rate for completing all four
pieces of data was so low, this is not a probable factor in this study.
The results indicated that use of the computerized diet analysis
program, as used in this course, did not positively or negatively
affect the students' attitudes toward nutrition.

DIETARY INTAKE

Average one-day nutrient intakes of the control and experimental
groups are summarized in Table 7. Results of ANOVAs indicated a
significant time effect (i.e., intake of these nutrients differ
between the pretest and posttest) across both groups (p<0.05) for
folacin, alcohol, vitamin A, and magnesium intakes (Table 8).

Table 8. Results of ANOVA for folacin, alcohol, vitamin A,
and magnesium intake from pretest to posttest

 

 

Nutrient F Signif
of F
Folacin" 6.76 <0.01
Alcohol‘ 9.02 <0.01
Vitamin A" 5.79 <0.05
Magnesium‘ 5.61 <0.05

 

0

time effect

61

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62

To further investigate in which group changes took place from
pretest to posttest, paired t-tests were conducted. In the control
group, intake of alcohol, folacin and vitamin A had significantly
decreased (p<0.05). In the experimental group there was also a
significant decrease in intake of alcohol (p<0.05) (Table 7). The
apparent decrease in alcohol intake for both groups was a positive
change. However, because there was no control group outside of the
nutrition course, it was not possible to measure whether this
decrease in intake was a result of the students taking the nutrition
course or not. Also because of the 10-week (one quarter) duration of
the study, it was not possible to measure whether alcohol intake has
seasonal variability, with warm or cold weather having more of an
effect on alcohol intake. There also may be a day of the week effect
for alcohol intake, with higher intakes existing in the middle or end
of the week. If the pretest and posttest diet records were kept for
different days of the week that might help explain the difference in
intake. There is no way of determining which days of the week were
recorded by students. This researcher also admits that recording of
one-day intakes is likely not an accurate representation of alcohol
consumption. Other research that has evaluated alcohol use among
college students has reported alcohol consumption in terms of
prevalence rate (percentage using alcohol), frequency of use and
quantity of use (Berkowitz and Perkins, 1986; Berkowitz and
Perkins, 1987; Davis.and Reynolds, 1990; Sarvela et al., 1988;
Valois, 1986). Different survey formats were used to gather the
alcohol consumption information in each of those studies.
Comparisons cannot be made between the reported one-day alcohol
intakes in the present study and the frequency and quantity of
consumption information from other studies.

The vitamin A intake of the control group (as well as the
experimental group) was more than twice the RDA at the pretest, so
the decrease in intake at the posttest brought the value closer to,
but still higher than the RDA. However the accuracy of mean vitamin
A intake is questionable. One-day intakes have been shown to be the
least accurate for vitamin A and C intake when evaluating individual
and also group data (Guthrie and Crocetti, 1985; Pao et al., 1985; St.

63

Jeor et al., 1983; Basiotis et al., 1987). This was one of the
concerns of one-day dietary intakes as stated in the literature
review section on 'Concerns with dietary assessment methods‘. It is
believed that the apparent difference in mean intake is actually a
reflection of the wide variation in day-to-day intake of vitamin A,
as shown by Guthrie and Crocetti (1985) and Basiotis et al. (1987).
The folacin intake of the control group was 85% of the RDA at the
pretest, and decreased to 75% of the RDA at the posttest. The
decrease in folacin intake might be due to the seasonality of food
sources of folacin. Green vegetables, which are more readily
available and affordable in the summer and early fall are a good
source of folacin. Although there was a significant difference in
magnesium intake across both groups, as indicated by ANOVA
procedures, paired t-tests for the control and experimental groups
individually showed no significant changes from pretest to posttest.
It has been observed in previous studies (Basiotis et al., 1987;
Beaton et al., 1979) that there are significant differences in dietary
intakes between males and females. In light of this, the dietary
intake data of the males and females in this study were compared by
t-tests to determine whether there were significant differences in
intake. Once the nutrients for which significant differences in
intakes between the sexes were identified, comparisons of those
nutrients were made within each of the sexes using ANOVA
procedures. However the data for the males and females needs to
interpreted with caution. Using Kraemer and Thiemann's (1988)
power analysis equation for the two-sample t-test, it was
determined that, for nutrients for which no significant difference in
intake was found, there were not enough females to have confidence
in the results at the 80% power level. For all nutrients for which no
significant difference in intake was found for the males, the n-size
was too small to have confidence in the results (using the 80%
power level). At the onset of the study, this researcher assumed
that using the students enrolled in HNF 102 would provide a large
enough sample size to obtain reliable results. However the sample
size was not as large as expected and in retrospect the data for
males and females were questionable taking into account the small

64

sample sizes. Therefore the power analysis was done post hoc in
order to have confidence in the results. The equation used and a
sample calculation are shown in Appendix G.

The average one-day nutrient intakes of the females at the pre-
and posttests are presented in Table 9. The ANOVAs indicated a
significant time effect (i.e., intake of these nutrients differ
between the pretest and posttest) for carbohydrate and magnesium
intake, and a significant group effect (i.e., intake of this nutrient
differs between the control and experimental groups) for saturated
fat intake (Table 10). Results of the t-tests indicated that the
saturated fat intake of the control group at the protest was
significantly greater (p<0.05) than that of the experimental group,
therefore this apparent difference in saturated fat intake is
accounted for by the pretest data (Table 9).

Table 10. Results of ANOVA for carbohydrate, magnesium
and saturated fat intake of the females (n-201)

 

 

Nutrient F Signif
of F
Carbohydrate” 5.67 <0.05
Magnesium“ 4.52 <0.05
Saturated fat‘ 4.13 <0.05

 

* group effect
** time effect

Results of the paired sample t-tests showed that for the control
group of females, a significantly higher intake (p<0.05) of
carbohydrates was found in the pretest as compared to the posttest
(Table 9). For the females in the experimental group, intake of
magnesium proved to be significantly higher (p<0.05) at the pretest
than at the posttest (Table 9). The magnesium intakewas 87%of the
RDA at the pretest and decreased to 71% of the RDA at the posttest.

65

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66

Changes in carbohydrate and magnesium intakes are unexplainable at
this time. This researcher believes that although there was a
statistically significant difference, the change in intake was not
likely to have any real biological significance.

The dietary intake of the females was less than 80% of the RDA
at both pretest and posttest for vitamin D, vitamin E, folacin, iron
and zinc. Data for vitamin D is incomplete however, because the
data base does not include milk fortified with vitamin D. Other
researchers also have found diets of college females to be below the
RDA for iron (Hernon et al., 1986; Hoffman, 1989; Khan and Lipke,
1982: Ostrom and Labuza, 1977). Hoffman (1989) also found that
67% of the women in her study consumed less than 75% of the RDA
for folacin. It was noted previously that the database contains
incomplete data for zinc, which might explain the seemingly low
zinc intake.

The mean one-day nutrient intakes of the males at pretest and
posttest are presented in Table 11. ANOVAs indicated a significant
group effect (i.e., intake of these nutrients differs between the
control and experimental groups) for intake of calories, total fat,
vitamin D, vitamin C, and sodium. A significant time effect (i.e.,
intake of this nutrient differs between the pretest and posttest)
was found for calcium intake (Table 12).

67

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68

Table 12. Results of ANOVA for calories, total fat, vitamin D,
vitamin C, sodium, and calcium intake of the males

 

 

(n-53)

Nutrient F Signif

ofF
Calories* 5.42 <0.05
Total fat‘ 6.90 <0.05
Vitamin D* 4.00 <0.05
Vitamin C' 6.77 <0.05
Sodium‘ 5.50 <0.05
Calcium“ 4.32 <0.05

 

* group effect
** time effect

Results of the t-tests showed that the sodium intake of the
experimental group at the posttest was significantly greater
(p<0.05) than that of the control group (Table 11). The sodium intake
of the control and experimental groups was much greater than the
Estimated Safe and Adequate Daily Dietary Intake (ESADDI)
recommendation. The vitamin D intake of the control group was
significantly greater at the posttest (p<0.05) than that of the
experimental group (Table 11). The other nutrients (calories, total
fat, vitamin C, and calcium) were not significantly different
between groups or from pretest to posttest using t-tests.

The dietary intake of the males was less than 80% of the RDA at
both pretest and posttest for vitamin E. Evaluation of vitamin E
intake was not included in other studies on dietary intake of college
students which were reviewed by this researcher. Hoffman (1989)
found that intake of calcium and folacin was less than 75% of the
RDA for some of the males in her study. The present study is in
agreement with other studies (Hernon et al., 1986; Khan and Lipke,
1982; Ostrom and Labuza, 1977) that found that mean intakes for
almost all nutrients met or exceeded the RDAs for college males.

69

From examination of the dietary intake data it was concluded
that use of the computerized diet analysis program, as used in this
course, had no more effect than the traditional lecture course.
There are several possible explanations for this, and these are some
of the limitations of this study. One explanation offered is that the
information given in this introductory nutrition course is of such a
general nature that to expect specific changes in dietary intakes
was unwarranted. Byrd-Bredbenner et al. (1988) noted that general
nutrition curricula help students comprehend and apply basic
nutrition principles rather than change specific dietary behaviors.

If changing dietary behaviors is the primary aim of school-based
nutrition education, then curricula need to target specific dietary
behaviors and employ change strategies specific to those behaviors.
Also, only two interventions using the computerized diet analysis
program were assigned by the instructor. This was likely not enough
intervention to cause a change in eating patterns of students.
Because of the study design, the researcher and instructor agreed
that no further attempts would be made by the instructor to
integrate into the course the information obtained from the dietary ‘
analysis or how to use this type of information. The objective of the
study was to evaluate whether or not recording of one-day intakes
and assessing those intakes by computerized diet analysis would
affect nutrition knowledge, attitudes or dietary intakes. If
additional information was given, it was theorized that the addition
of information could affect the outcome of the study.

Informal reports from students indicated that many completed
the dietary analyses mainly for the credit rather than for self-
assessment of their intake. Another probable explanation for lack of
change is that the nine-week period from the first diet record
assignment to the second is not enough time to make significant
changes in one's diet. Brush et al. (1986) noted that 'any change in
usual intake is difficult because changes are not made or maintained
easily'. The knowledge gain from the course was the immediate
outcome, and changes in attitude and dietary behavior were possibly
more distant and not readily apparent. Also it is well recognized
that it is difficult to change dietary habits because eating is not

70

only a physiological function but also a social, emotional,
environmental, cultural and habitual function. This is true for the
general population, and this researcher believes it is especially true
for these college students. Of the students in the study population
70% lived in the residence halls. Therefore they ate meals from the
cafeterias with peers and may have had a more difficult time making
changes in their diets. They were subject to the cycle menu system
and students might have felt as though they had little control over
their food choices. Other researchers (Beerman et al., 1990;
Gottschalk et al., 1977; Jakobovits et al., 1977; O'Leary and Lee,
1975) have found that living in residence halls and subscribing to
meal plans does affect college students' dietary intake. College
students are also subject to much peer pressure in this situation and
time in their lives.

NUTRITIONAL ADEQUACY

The Mean Adequacy Ratio (MAR) values for both control and
experimental groups at pretest and posttest are presented in Table
13. Because there were no significant differences between the
MARs' of the control and experimental groups at pretest and posttest
the groups were combined and an overall MAR was determined for
the pretest and posttest. The combined group mean MAR at the
pretest was 79 with a standard error of 1.1. At the posttest the
mean MAR was 77 with a standard error of 1.1. This indicator of
diet quality supports the previously drawn conclusion that there was
no change in dietary intakes from pretest to posttest.

71

Table 13. Pretest and posttest MAR of control and experimental

 

 

 

 

groups
Pretest __ Pos_tt_e_st
Controi Experimental Control Experimental
Mean 80 76 78 75
E 1.2 1.8 1.1 2.0
range‘ 28-100 34-99 40-100 24-100

 

‘ MAR is the average of NARs truncated at 100,
and ranges from 0 to 100

Table 14. Pretest and posttest MAR for females“ and males“

 

 

 

 

 

Pretest Posttest
Wmales Males T-‘émales MaTes
Mean 78 87 77 86
SE 1.1 1.2 1.1 1.7
ran__gg 29-100 59-99 25-100 42-100
* n-201
** n-53

MAR scores also were determined for females and males at
pretest and posttest (Table 14). From the MAR scores, it appears
that the males had better diets than the females. This is generally
true because of the greater calorie consumption of the males. When
examining the range of scores it looks as though the females had
much lower scores than the males. However when the histograms
for the females were examined, there were two low MAR scores in
the 20's at both pretest and posttest, and the normal range started
at 49 and 42 for the pretest and posttest respectively, making the
range of scores much closer to the males.

72
VARIABLES RELATED TO MAR

The regression equation formulated to show associations
between the dependent variable (MAR) and independent variables
(age, gender, place of residence, class level, and body mass index)
was as follows:

MAR-b1(age) + b2(gender) + b3(residence) + b4(level) + b5(BMl) + 0

Results of the backwards method of regression analysis for the
pretest indicated that two of the independent variables, gender and
age were significant (p<0.05) predictors of nutritional adequacy
(Table 15).

Table 15. Results of the regression analysis for the variables
gender and age to predict pretest MAR

 

 

Variable b‘ Beta“ t Signif
oft ‘
Gender 11.064 284 4.590 <0.001
Age .944 85 2.422 <0.05
Constant 51.950 7.127 <0.001

 

* b-regression coefficient ..
“ Beta-standardized regression coeffrcrent

The regression equation can thus be shown as follows:

MAR - 11.064(gender) + 0.944(age) + 51.950

In this equation the independent variable gender contributed more to
the prediction than age, based on the Beta values. The adjusted
multiple correlation coefficient (R) for this equation was 0.285.
The adjusted R-squared value was 0.081. Therefore the variables

73

gender and age accounted for 8 percent of the variance in pretest
nutritional adequacy. BMI, class level, and place of residence did not
account for a significant amount of the variability in nutritional
adequacy. '

The ages of students in the study group are presented in Table 16.
From examination of the data for MAR scores by age, it appears that
the younger students, ages 17-21 had better diets than the students
over 21, explaining why age was a significant factor in predicting
nutritional adequacy. Students 21 and under had the highest
frequency of MAR scores in the 90's. Students over 21 generally had
MAR scores in the 80's.

Table 16. Percentage of the total study population by age groups

 

 

17-20 21 and over
W (7..)
Pretest 84 16
Easttest 84 16

 

Table 17 shows the results of the regression analysis for
posttest data. The adjusted multiple correlation coefficient (R) for
this equation was 0.294 and the adjusted R-squared value was 0.087.
Therefore the variable gender accounted for 9 percent of the
variability in nutritional adequacy. Age was close to significance
for the posttest data (p-0.069), showing some consistency with the
pretest equation. However, BMI, class level, age and place of .
residence did not account for a significant amount of variance in

posttest nutritional adequacy.

74

Table 17. Results of the regression analysis for the
variable gender to predict posttest MAR

 

 

Variable b * Beta“ t Signif
of t

Gender 11.326 .292 4.784 <0.001

Constant 54.774 7.986 <0.001

 

* b-regression coefficient
*" Beta-standardized regression coefficient

The regression equation is as follows:
Mar -11.326(gender) 4» 54.774

The results from both pretest and posttest suggest that gender is
the strongest of the available predictors of diet quality. This is
supported by previous studies (Basiotis et al., 1987; Beaton et al.,
1979) that found a difference between the dietary intakes of males
and females. Beerman et al. (1990) and Haseba and Brown (1968)
also found that gender was one of the factors related to dietary
intake. In the present study the males had better MAR scores than
the females. The results indicated that most of the variance in
nutritional adequacy was not predicted by variables included in this
study. Other researchers have identified factors that influence
dietary intake of college students. These included body weight
(Wakefield and Miller, 1971), length of time in college (Brown,
1967), subscription to a meal plan (Gottschalk et al., 1977;
Jakobovits et al., 1977; O'Leary and Lee, 1975), patterns established
prior to attending college (Stasch et al., 1970), and place of
residence while in college (Beerman et al., 1990; O'Leary and Lee,
1975)

75

RELATIONSHIP AMONG NUTRITION KNOWLEDGE, ATTITUDES
AND DIETARY INTAKE

To determine whether there was a relationship between nutrition
knowledge, attitudes, and dietary intake, correlations were
determined at pre-and post-tests (Table 18). Results showed no
significant correlations between any of the three variables at the
pretest and posttest, with the exception of a significant (p<0.01)
correlation between knowledge and dietary intake at the pretest
(Table 18). The negative correlation between knowledge and
attitudes and knowledge and dietary intake at the posttest is a
result of the nutrition knowledge scores increasing from pretest to
posttest and the attitude scores and dietary intake remaining the
same.

Table 18. Correlations between nutrition knowledge,
attitudes, and dietary intake

 

 

 

Variable Pretest Posttest
Knowledge- .09 -.06
Attitudes p-.078 p-.158
Knowledge- .16 -.02
Dietary intake p-.006 * p-.351
Attitudes- .09 .06
Dietary intake p-.073 p-.179

‘ p<0.01

Reviewing the literature relevant to this study, Brush et al.
(1986) found a significant correlation between nutrition attitudes
and dietary behaviors for adults aged 19 to 65 years. This was in

76

contrast to the findings in this study. Although the mean age (42.6
yrs.) was somewhat higher than the mean age of college students
(19.8 yrs.) in the present study, participants in the Brush et al. study
were from the general adult population rather than a more specific
target group, and the study design consisted of a pretest,
intervention and posttest, similar to the present study.

Also in contrast to this study, Werblow et al.(1978)
demonstrated a significant positive correlation between nutrition
knowledge and attitudes of female collegiate athletes. Schwartz
(1975) supported the relationship of nutrition knowledge and
attitudes, and also nutrition attitudes and practices in female high
school graduates four years after high school. Because there was no
direct relationship between nutritional knowledge and practices,
Schwartz proposed that attitudes mediate both knowledge and
dietary practices.

Knowledge and dietary intake were significantly correlated in
few studies (Douglas and Douglas, 1984; Foley et al., 1983; Soliah et
al., 1983). Douglas and Douglas (1984) found a positive correlation
(r-.35) between knowledge and dietary practices of their male high
school athletes, and a weak positive correlation (r=.15) between
knowledge and dietary practices for their female high school
athletes. Foley et al. (1983) found positive correlations between
knowledge and nutrition practices of fifth grade students. Soliah et
al. (1983), studying elementary school teachers, also found a
positive correlation between knowledge and nutrition practices.
However both Foley et al. (1983) and Soliah et al. (1983) assessed
dietary practices by responses to statements about dietary
practices rather than actual diet records.

Based on the most prevalent relationship found in the literature
cited, this researcher made the assumption that nutrition knowledge
and attitudes would be correlated. However the data do not support
this assumption. This is perhaps due to the scores for the attitude
assessment. The students achieved a mean score around 50% of the
maximum possible score on the attitude scale at both pretest and
posttest. It was also assumed that knowledge or attitudes alone
was likely not enough to influence dietary behaviors. However, if

77

knowledge and attitudes both improved, this improvement might
have a positive effect on dietary intake. This researcher theorized
that if knowledge and attitudes improved and changes in dietary
intake were found, a correlation between knowledge or attitudes and
dietary intake at the posttest would be more likely. However there
was no significant improvement in nutrition attitudes and no major
significant changes in dietary intake, and no significant correlations
found among knowledge, attitudes or dietary intake at the posttest.

Ajzen and Fishbein (1980) have emphasized that when trying to
understand or predict behaviors, the specificity of the variables
must be carefully considered. The less specifically the variables
relate to the behavior, the less likely they are to exlain the behavior.
Guiry and Bisogne (1986), studying knowledge of caffeine, attitudes
and practices in young women 15-30 years old stated that the lack
of specificity between caffeine knowledge and the general attitude
statements in their study probably accounted for the lack of
association found. It may be true that for the present study the lack
of specificity between the nutrition knowledge questions and the
general attitude statements accounts for the lack of a relationship
between nutrition knowledge and attitudes. Shepherd and Stockley
(1987) noted that nutrition education will not be effective in
modifying the intake of specific nutrients in the population if
nutrition knowledge relates to general attitudes toward nutrition
and general nutrition practices but not to the eating of specific
foods.

The basic nutrition course utilized in this study was designed to
cover a variety of topics and was therefore somewhat general in
nature. The nutrition knowledge test administered was intended to
follow the design of the course, including questions on most of the
tapics covered. Therefore questions were specific, but covering
many topics it could be considered general in nature. The attitude
items assessed general attitudes related to 'concern about nutrition'
and ‘flexibility/rigidity' in attitudes toward nutrition, rather than
relating to the nutrition knowledge questions. If the nutrition
knowledge and attitude assessments were of a specific nature, and
were related to each other, then a correlation would more likely

78

have been found. Also the course and the nutrition knowledge and
attitude assessments were not geared toward or related to any
specific dietary behaviors, therefore helping to explain the lack of
relationship between dietary intake and knowledge or attitudes.

Assumptions

Before beginning the data collection phase of this study several
assumptions were made by this researcher, based on findings from
other studies and ideas about this project. The assumptions were as
follows:

1) That students in both sections of HNF 102 had similar
backgrounds and characteristics.

2) There is a relationship among nutrition knowledge, attitudes and
dietary intake. Based on the literature reviewed, this researcher
assumed that for this study at least knowledge and attitudes would
be correlated.

3) That students honestly answered the nutrition knowledge and
attitudes pretest and posttest.

4) That the students in the experimental and control groups
honestly recorded all food and beverage items consumed, without
fear of evaluation. It was stressed that the students would not be
graded or evaluated on the adequacy of their diet, and that the
assignment would be more meaningful and informative for them if
they were honest.

5) That the students accurately estimated portion sizes. Guthrie
(1984) reported, however, that students and university employees
ages 18 to 30 had difficulty estimating portion sizes, with errors
greater than 50% for both under-and overreporting of many food
items. For this study it was assumed that errors in estimating
portion sizes were consistent from pretest to posttest and from
group to group, therefore, minimizing error in making comparisons.

6) That apprOpriate substitutions were made when consumed food
items were not in the database of the diet analysis program.

79

80

7) That minimal errors were made when entering foods into the diet
analysis program because foods did not have to be entered by code
numbers.

8) That the students were interested in self-evaluation of their
diets in order to improve their dietary habits.

9) That the students used the diet analysis software as the
researcher intended.

LIMITATIONS

This study has limitations which should be addressed and
considered when designing further studies to evaluate nutrition
knowledge, attitudes and behaviors.

1) The basic study design intended that recording dietary intakes
and assessing the intakes via a computerized diet analysis program,
along with the information gained in HNF 102, would heighten
students' awareness of the need for change, and that nutrition
knowledge, attitudes and dietary intakes would be affected
positively. The course and assignment were designed to increase
nutrition knowledge. However, there is no evidence that the course
or assignment of recording intakes and evaluating them via computer
was designed specifically to change the nutrition attitudes
measured. Therefore to expect changes in attitudes when that was
not a focus of the course or assignment was improbable. It was not
a course objective to change dietary intake, but this was an
objective of the diet analysis assignment. However to expect a
significant change in intake after completing one dietary
assessment was questionable.

2) The participation rate was low for completing the four parts
necessary for this study (nutrition knowledge and attitudes pretest
and posttest and pre and post one-day diet records). The pretest and
posttest were not required as part of the course, therefore some
students did not complete one or both of the tests, and this
contributed to incomplete data for many students. Dietary data
might not have been saved by a number of students, contributing to
incomplete diet records. The diet record assignment and nutrition
knowledge and attitude posttest given near the end of the term
might have had a low priority for students, because of other
commitments for completing courses. This is evidenced by the
lower participation rates at the posttest compared to the pretest

81

82

(Eighty-two and 83% of students in the control and experimental
groups respectively completed pretest; 68 and 64% of students in
the control and experimental groups respectively completed
posttest; 83 and 92% of students in control and experimental groups
respectively completed the first diet record assignment; 68 and 66%
of students in control and experimental groups respectively
completed the second diet record assignment).

3) There was a lack of specificity between the nutrition knowledge,
nutrition attitudes and dietary intake measured. The general
nutrition knowledge questions were unrelated to the nutrition
attitudes items. Also, the knowledge and attitudes measured might
have related to the diet in general, but not to intake of specific
foods.

4) It is questionable whether significant changes in attitudes or
dietary intake are to be reasonably expected in an eight to nine-
week period. The steps related to the change process might or might
not occur within the time of the intervention and assessment. The ‘
course and diet analysis assignment act as stimulants, and attitude
and dietary changes may be ongoing after the course. Therefore the
posttest scores and second diet record results might underestimate
positive changes.

5) The recording and analyzing of one-day food records depends on
honesty of self-reported food consumption, ability to correctly
record amounts consumed, and ability to correctly identify foods or
adequate substitutions from the dietary analysis program database.
Also, as far as evaluation purposes for this study, one-day intakes
have been shown to adequately represent mean group intake for large
groups. The one-day intake was used because it was assumed that
the response rate would be better than when using a lengthier
method. However, the purpose of the assignment was to have
students use their dietary assessments as a basis for making
appropriate changes in their diets. Previous research has shown
that one-day intakes are not an accurate representation of individual

83

diet intake. A three-day record would have been more appropriate
for individual intake.

SLMMARY

1) Nutrition knowledge: Scores of both groups (Control, n-177;
Experimental, n-77) increased significantly (p<0.001) from pretest
to posttest (Pretest: 7.9, 7.4; Posttest: 128,149, respectively).
However there were no differences in nutrition knowledge between
the groups at the posttest.

2) Nutrition attitudes: There were no changes in nutrition attitudes
from pretest to posttest for either group, and no significant
differences between the groups at the posttest.

3) Dietary intake: There were no significant differences as measured
by one-day diet records, between the dietary intakes of the groups
at both the pretest and posttest. However within the groups from
the pretest to the posttest significant differences were found. The
control group significantly decreased their intake of alcohol,
folacin, and vitamin A (p<0.05). The experimental group _
significantly decreased their intake of alcohol (p<0.05). However, it
is doubtful that one-day intakes give an accurate representation of
alcohol intake. Decreases in folacin intake might be due to the
seasonal availability of good sources of folacin. Also one-day
intakes have been shown to be inaccurate for evaluating vitamin A
intake.

4) Dietary intake of males and females : When the dietary data were
examined for males and females separately, some significant
differences were found. However results must be interpreted
cautiously due to the small sample sizes (Females, n-201; Males,
n-53)

5) Predictors of diet quality: At the pretest gender and age were
shown to be the best of the available predictors of diet quality,
accounting for 8% of the variance. Class level, place of residence,
and BMI were insignificant predictors of diet quality. At the

84

85

posttest gender was the only variable that predicted diet quality,
accounting for 9% of the variance. The males had better MAR scores
than the females at both pretest and posttest.

6) Correlations among nutrition knowledge, attitudes, and dietary
intakes: No correlations were found among the three variables, with
the exception of a significant correlation (p<0.01) between nutrition
knowledge and dietary intake at the pretest.

CONCLUSIONS

The hypothesis of this study was that students' recording of one-
day dietary intakes and use of the computerized dietary analysis
program for dietary assessment would heighten the students'need
for change and therefore would improve nutrition knowledge and
attitudes and affect dietary behaviors. However, self-recording of
intakes and self-evaluation using the computerized dietary analysis
program had no more effect on general nutrition knowledge and
attitudes and dietary intake than the traditional course. Therefore
the hypothesis of this study can be rejected. It is possible that the
assumption that students are interested in self-evaluation of their
diets in order to make positive changes is wrong. The students' main
concern might be cognitive gains and earning a good GPA.

Gender was the only available demographic variable that
significantly predicted MAR. Gender accounted for only 9% of the
variability in dietary quality at the posttest, leaving most of the
variability unexplained. The males had better MAR scores than the
females. The other available demographic and environmental factors,
(i.e., age, class level, place of residence, and body mass index) were
insignificant predictors of MAR.

There was no relationship found among nutrition knowledge,
attitudes and dietary intake, with the exception of a significant
correlation (p<0.01) between nutrition knowledge and dietary intake
at the pretest. The lack of relationship may be due to the general
nature of the nutrition knowledge test, the attitude questionnaire,
the fact that neither the knowledge test or attitude questionnaire
were related to any specific dietary behavior, or other factors.

86

RECOMMENDATIONS

Based on the results of this research, this author has several
recommendations to make regarding nutrition education, further
research on the relationship between knowledge, attitudes, and
behavior, use of the computerized dietary assessment, and research
on factors that influence dietary intake.

Effective education requires assessment of the nutrition
knowledge, dietary practices, and concerns of a population.
Gillespie (1987) indicated that "the more narrowly targeted a
nutrition message, the more effective it will be.”

Bemmmendatmu: A survey, questionnaire or pretest could be

given each term to students taking the introductory nutrition course
to determine their nutrition knowledge, dietary practices and
concerns. This information could be used to determine whether the
goals and objectives of the course are consistent with students'
needs. Updates in goals and objectives could continually be made as
needed, to better meet the needs and concerns of students.
Frequently nutrition classes are taught under the assumption that
the better informed peOple are, the more likely they are to make
better choices in the selection of their food. From this research and
previous studies it is clear that knowledge is a necessary but not
necessarily sufficient factor in behavior change. Obviously
increased knowledge is a major objective of nutrition education.
The question is whether behavior change is a legitimate objective,
especially in short-term education programs such as the 10-week

basic nutrition course.

Emma: Because significant changes in dietary intake

are not likely to occur during the 10-week nutrition course, behavior
change, as an objective of the introductory nutrition course, should
not focus on statistically significant changes. The evaluation of
dietary intakes could look for changes or trends in intake that have
been determined to be important (e.g. decreases in fat intake,
decreases in sodium intake, increases in iron intake, etc.). The
computerized dietary assessment could be utilized as a tool to focus

attention on the desired behavior changes.
87

88

It was suggested by the results of this study that behavior
change is not an immediate outcome of a basic nutrition course, but
rather more distant.

3W: Research on dietary intake should be on a longer
term basis than 10 weeks. Students that consented to participate,

after completing the introductory nutrition course, could complete
food records and computerized dietary assessments over a specified
period of time, such as six months.

Nutrition education programs and courses need to define
specifically the goals and objectives of the program or course and
which areas of knowledge and attitudes will be addressed.
Wm: Given the course goals and objectives, the
relationship between nutrition knowledge, attitudes, and dietary
intake needs to be further examined by using instruments which
measure variables specifically related to course goals. The
knowledge and attitude measurement instruments should be very
specific, and should relate specifically to the nutrition information

taught

W: Further evaluation is needed to determine

whether the assignment of recording dietary intakes and self-
evaluating intakes is consistent with students' concerns. The
students main concern might be attainment of a good grade for the

course rather than changes in dietary intake.

WM: Further studies are needed to determine how or

whether the computerized dietary assessment program can be better
integrated into the class lecture and activities to affect students'
dietary intake. Variables such as interpretation and application of
information from dietary assessments, more frequent self-
assessments, and instruction on estimating portion sizes and how to
complete a food intake record should be evaluated to determine the

usefulness of the assignment on improving students' diets..
W: The students need to be accountable for their
dietary assessments. Points should be given that could significantly
affect their course grade for completion of an assignment evaluating
their personal information. Students need to make a judgment about

89

the adequacy of their diets and assess where they could make
improvements.

mm: In light of the limitations of one-day food

records for individual assessment, in order for the students to
adequately evaluate their diets a three-day diet record would be

more desirable.

WM: Data from this study and others on college

students needs to be further examined to determine the role of
various factors on dietary intake. lf factors that influence dietary
intake can be more well defined, those factors could help determine
the goals and objectives of nutrition education. For example, place
of residence has been identified as one factor that influences food
intake of college students (Beerman et al., 1990; O'Leary and Lee,
1975). If students on meal plans eat better quality diets than off-
campus students, then nutrition education programs should target
the off-campus students. This author believes that place of
residence, body image, emotional reaction to food, socioeconomic
status, level of physical activity, and advertising are some
important variables that should be studied in the future.

APPENDICES

APPENDIX A

90

MICHIGAN STATE umvsasrryAppendlx A

 

UNIVERSITY COMMIT!!! ON RESEARCH INVOLVING ' EAST LANSING 0 MICHIGAN 0 «ON-III!
HUMAN SUBJECTS (UCIIIIS)
2.6 DENY "ALL

(3!?) 333-9130
September 30, 1988 j W
I
Sherry Nied '1
Room 208 Food Science Bldg. /

Dear Ms. Nied: /

Subject: ”EFFECT OF AN INTERACTIVE COMPUTERIZED DIET
ANALYSIS PROGRAM IN NUTRITION EDUCATION “(Bi §§-,‘I§j"

The above project is exempt from full UCRIHS review. I have reviewed
the proposed research protocol and find that the rights and welfare of
human lsubjects appear to be protected. You have approval to conduct the
researc 1.

You are reminded that UCRIHS approval is valid for one calendar year. If
you plan to continue this pro'ect beyond one year, please make provlsmns
for obtaining appropriate UC’l’RIHS approval

26.1952.

Any changes in procedures involving human subjects must be reviewed by
the UCRII'IS prior to initiation of the change. U.CRIHS must also be
notified promptly of any problems (unexpected side effects, complaints,
etc.) involving human subjects during the course of the work.

Thank on for bringing this project to our attention. If we can be of any
future elp, please do not hesitate to let us know.

Singerely,

 
 
 

 

.lt hn K. I udzik, Ph.D.
Clair, UCRIHS

JKlI/sar
cc: W. Song

"3 U I: .- Affirmative Anion/Equal Opportunity Inuit-lino

APPENDIX B

92

F‘. Food Science and
_ Human Nutrition

Michigan State Universuy East Lansing. Mlchlgan 488244224

Consent Form For HNF 102 Students (Sec. 3)

As a course requirement for HNF 102, I will record a
one-day dietary intake at the beginning and end of the
quarter and run a computerized diet analysis program, for
assessment of my intake. The computer analysis is expected
to take approximately thirty minutes. I will also complete
pre- and post-term questionnaires related to nutrition
knowledge and attitudes. I understand that I will receive a
small credit for completing the assignment, as indicated in

the course syllabus. .
I understand that, with my consent, information obtained

from my pre- and post—term questionnaires and dietary
assessment will be used for research purposes, and in an
attempt to improve this course. I understand that my
responses will be kept confidential and anonymous. My name
will not be used in any part of the study. The results of
the project will be available to me at my request upon

completion of the study.
I understand that I am free to decide not to have my

data used in this project, without penalty. This will in no
way affect my grade for the course. I also understand that I
can change my decision at any time.
I understand that Sherry Nied, a graduate student (353-
9736) and Dr. Won Song (353-9604), in the Department of Food
Science and Human Nutrition are conducting this study
independent of the instructor. I will be able to ask the
researchers questions related to the project anytime during

the term.

I have read the above statements and agree to have my data

used for research purposes.

Signed

 

Print Name

 

Date

 

Thank you very much.
Sherry Nied 353-9736

Dr. Won Song 353-9604
Dept. of Food Science and Human Nutrition

Rm. 208 Food Science Bldg.

MSU is on ”(mm Anion/Equal W W

APPENDIX C

9 3
Appendix C

HNF 102
Sec. 2, 3
Bond (F 88)

HNF102 PRETEST

Fill in your name, student number and section number on the answer sheet.

Read the following questions and choose the best answer for the multiple choice questions. For
questions 23-45, select the answer that reflects your degree of agreement with each
statement. THIS PRE-TEST WILL IN NO WAY AFFECT YOUR GRADE FOR THE
COURSE.

1. The correct match to the name of the B vitamin is

a. Bl -thiamin

b. B2-pyridoxine
c. BS-niacin

d. B12-riboflavin
e. B6-cobalamin

2. Which of the following is produced by bacteria in the intestinal tract?
a. vitamin A
b. vitamin K
c. ascorbic acid
d. vitamin E

3. A deficiency of iodine may be seen as
a. anemia
b. spoon-shaped finger nails
c. goiter
d. skin rashes

4. Absorption rate of iron .
a. is increased when the body's need for iron is Increased

b. is approximately 50%
c. in the heme form is lower than in iron from plant sources
d. in the normal diet is so high that it reduces absorption of copper

5. The stored form of carbohydrates in the body is called
a. glycogen
b. insulin
0. fat
d. muscle

6. Proteins are essential in the diet

a. as the major source of energy .
b. to provide nitrogen for growth and tissue development

c. to furnish the only source ofhydrogen
d. to provide essential fatty acids

94

7. Elevated concentrations of this Iipoprotein are associated with a decreased risk of
cardiovascular disease

a. low density Iipoprotein

b. high density Iipoprotein

c. lecithin

d. linoleic acid

8. The major form of fat in the diet is
a. cholesterol
b. lecithin
c. triglyceride
d. polyunsaturated fat

9. Of the following, which is the best source of polyunsaturated fat?
. soybean oil
animal fat
margarine
shortening
coconut oil

999.6»

10. The correct function of the organ In the digestive tract is
a. the mouth is the major site of carbohydrate digestion
b. the small intestine is chiefly a storage reservoir
c. the stomach is the site where most digestion occurs
d. the pancreas secretes amylase and lipase into the small intestine

11. The specific dynamic action reflects the increase in energy expenditure
a. caused by respiration
b. caused by circulation
c. caused by glandular activity
d. following ingestion of food

12. Normally the most energy Is expended for
a. Basal Metabolic Rate (BMR)
b. physical activity
c. thermogenesis
d. digestion of food

13. Pica is
a. a vitamin deficiency
b. craving for non-foods such as clay, laundry starch, sand and

newspaper
0. most commonly seen in men and adolescent boys

d. a recommended mineral supplement at the level of the RDA

14. Which of the following is considered a nutrient?
a. carbohydrate
b. lipids
c. water
d. all of the above

95

15. Your breakfast included a fried egg, whole wheat toast, and orange juice. Which Basic Four
Food Group was not represented?

16.

17.

18.

19.

a. meat group

b. dairy group

c. grain group

(1. fruit and vegetable group

During pregnancy
a. the increased need for energy exceeds the increased need for protein

and minerals

b. two extra servings of iron-enriched bread are sufficient

to meet the increased iron needs

c. the mother needs to double the amount of food she eats by

the sixth month to meet the baby's needs

d. it is recommended that the average mother gain approximately 25-30 pounds

The requirements for water based on 1000 kcals expended is the greatest for
a. infants
b. adults
c. teenagers
d. elderly

The Recommended Dietary Allowances for fat and carbohydrate
a. have not been established
b. are the same for both
c. depend on ideal body weight
d. are based on basal metabolic rate

Which food group has the highest percent of complex carbohydrate?
a. milk group
b. meat and alternates group
0. fruit and vegetables group
d. cereal and grains group
e limited extra

20. The general guidelines in nutrition recommend what percent of your calorie intake should
be from fat?

21.

a. 15%
b. 20%
c. 30%
d. 40%
e. 55%

The adult recommendation from the Basic Four Food Plan is
a. 4 servings each day from the dairy group
b. 2 servings each day from the cereal group
c. 2 servings each day from the fruit and vegetable group
d. 2 servings each day from the meat group

22. If you consumed 100 grams of fat and your total caloric int

96

percent of your energy Intake was from fat?

23.

24.

25.

26.

27.

28.

29.

30.

31.

32.

35.

a. 5%
b. 20%
c. 22%
d. 50%

I believe that diet is a major controlling
factor when it comes to maintaining health
and controlling disease.

I feel the foods I eat now will affect my
future health.

I am concerned about eating nutritious
foods throughout the day.

Nutrition is extremely important during pregnancy.

I feel that I need to learn more about
good nutrition.

If a person is not eating well his/her health
will surely suffer.

I feel that as long as I am maintaining my
weight, I don't have to worry much about
nutrition.

As long as the doctor doesn‘t say much to
me about nutrition, I don‘t think I need to

worry about it.

I feel that all college students need to
take at least one course in nutrition.

I feel that if I drink milk I don’t have
to worry about nutrition.

Nutrltlon is important and one should not
be careless about it.

Even If I take vitamins, I feel that I should
be concerned about the foods I eat.

I would fix more nutritious meals if I knew
what to prepare.

ake was 1800 kcals, what

 

5* c VAR,
‘b\ 5 “3'22.

Disast‘u,

No
OP\V\ \Ov"

agree

5+ {‘CIM3I1
O S." c e

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

36.

37.

39.

40.

41.

42.

43.

45.

46.

47.

48.

97

For better health, I would be willing to try
a food I hadn't eaten before or several foods
over a period of time.

Exploring several methods of food preparation
is desirable.

I think that food habits should be flexible
enough to vary with a new situation.

I like for my family to stick to the old
favorite meals, rather than mess them up
with new and different kinds of foods.

Unfamiliar foods interest me.

If I am satisfied with foods I eat, I see no
reason for me to change.

I believe that the person who gets the most
satisfaction out of eating is the one who
sticks to the foods that are familiar.

Trying new and different foods appeals to me.

I would be willing to spend time in making
nutritious feeds available for myself and/or
family instead of eating convenience foods
of low nutritional quality.

I would be willing to try an unfamiliar food
at least once.

Where do you currently reside?

a. Residence Hall

b. Off-campus apartment or house
c. Fraternity/Sorority

d. Home with parents

e. Other

What class level are you?

a. Freshman

b. Sophomore

c. Junior

d. Senior

e. Graduate student or other

 

 

a: 3 U a,

I
(it? ‘93 og_ 24. ago
wacrflfza c: we
.._..,

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Have you previously enrolled in any other nutrition or healthwelated

courses?

a. Yes

b. No

If yes, please list:

 

98

HNF 102
Sec. 2
Bond (F 88)

HNF 102 POSTIEST

statement. THIS POSTI’EST WILL IN NO WAY AFFECT YOUR GRADE FOR THE
COURSE.

1. The correct match to the name of the B vitamin is
a. Bl-thiamin

B2-pyridoxlne

BS-niacin

B12-riboflavin

BS-cobalamin

9'

99.0

2. Which of the following is produced by bacteria in the intestinal tract?
a. vitamin A
b. vitamin K
c. ascorbic acid
d. vitamin E

3. A deficiency of iodine may be seen as
a. anemia
b. spoon-shaped finger nails
c. goiter
d. skin rashes

4. Absorption rate of iron
a. is increased when the body's need for iron is increased
b. is approximately 50%
c. in the heme form is lower than in iron from plant sources
(I. in the normal diet is so high that it reduces absorption of copper

5. The stored form of carbohydrates in the body is called
a. glycogen
b. insulin
c. fat
d. muscle

6. Proteins are essential In the diet
a. as the major source of energy
b. to provide nitrogen for growth and tissue development
c. to furnish the only source of hydrogen
d. to provide essential fatty acids

99

7. Elevated concentrations of this Iipoprotein are associated with a decreased risk of
cardiovascular disease

a. low density Iipoprotein

b. high density Iipoprotein

c. lecithin

d. linoleic acid

8. The major form of fat in the diet is
a. cholesterol
b. lecithin
c. triglyceride
d. polyunsaturated fat

9. Of the following, which is the best source of polyunsaturated fat?
. soybean oil

animal fat

margarine

shortening

coconut oil

99.0.0»

10. The correct function of the organ in the digestive tract is
a. the mouth is the major site of carbohydrate digestion
b. the small intestine is chiefly a storage reservoir
c. the stomach is the site where most digestion occurs
d. the pancreas secretes amylase and lipase into the small intestine

11. The specific dynamic action reflects the increase in energy expenditure
a. caused by respiration
b. caused by simulation
c. caused by glandular activity
d. following ingestion of food

12. Normally the most energy is expended for
a. Basal Metabolic Rate (BMR)
b. physical activity
c. thermogenesis
d. digestion of food

13. Pica is
a. a vitamin deficiency
b. craving for non-foods such as clay, laundry starch, sand and
newspaper
c. most commonly seen in men and adolescent boys
d. a recommended mineral supplement at the level of the RDA

14. Which of the following is considered a nutrient?
a. carbohydrate
b. lipids
c. water
d. all of the above

100

15. Your breakfast included a fried egg, whole wheat toast, and orange juice. Which Basic Four
Food Group was not represented?

16.

17.

18.

19.

a. meat group

b. dairy group

c. grain group

d. fruit and vegetable group

During pregnancy
a. the increased need for energy exceeds the increased need for protein

and minerals

b. two extra servings of iron-enriched bread are sufficient

to meet the Increased Iron needs

c. the mother needs to double the amount of food she eats by

the sixth month to meet the baby's needs

d. it is recommended that the average mother gain approximately 25-30 pounds

The requirements for water based on 1000 kcals expended is the greatest for
a. infants
b. adults
c. teenagers
d. elderly

The Recommended Dietary Allowances for fat and carbohydrate
a. have not been established
b. are the same for both
c. depend on ideal body weight
d. are based on basal metabolic rate

Which food group has the highest percent of complex carbohydrate?
a. milk group
b. meat and alternates group
0. fruit and vegetables group
d. cereal and grains group
e. limited extra

20. The general guidelines in nutrition recommend what percent of your calorie intake should
be from fat?

21.

a. 15%
b. 20%
c. 30%
d. 40%
e. 55%

The adult recommendation from the Basic Four Food Plan is
a. 4 servings each day from the dairy group
b. 2 servings each day from the cereal group
c. 2 servings each day from the fruit and vegetable group
d. 2 servings each day from the meat group

101

22. If you consumed 100 grams of fat and your total caloric intake was 1800 kcals,what
percent of your energy intake was from fat?

23.

24.

25.

26.

27. I feel that I need to learn more about
good nutrition.
28. If a person is not eating well his/her health

a. 5%
b. 20%
c. 22%
d. 50%

I believe that diet is a major controlling
factor when it comes to maintaining health
and controlling disease.

I feel the foods I eat now will affect my
future health.

I am concerned about eating nutritious
foods throughout the day.

Nutrition is extremely important during pregnancy.

will surely suffer.

29. I feel that as long as I am maintaining my
weight, I don't have to worry much about
nutrition.

30. As long as the doctor doesn‘t say much to

31. I feel that all college students need to

take at least one course in nutrition.
32. I feel that If I drink milk I don't have

to worry about nutrition. l
33. Nutrition is important and one should not

be careless about it. ‘
34. Even if I take vitamins, I feel that I should :

beconcemed aboutthefoodsleat ~.
35. I would fix more nutritious meals if I knew

me about nutrition, I don't think I need to
worry about it.

what to prepare.

 

70 2’. I

’ L- ( .

373% ~32 «,5 3 {‘3-
6 V /

8".” C 04:. (5’) :1?
3A 1410 \‘5 «‘0

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

36.

37.

39.

40.

41.

42.

43.

44.

45.

46.

47.

48.

49.

50.

102

 

 

 

 

u r
., In“ ’ 8 9 U .
For better health, I would be willing to try a 213’" 3 ‘P z I: g" ff— 3” ..

 

 

a food I hadn't eaten before or several foods
over a period of time.

 

Exploring several methods of food preparation
is desirable.

 

I think that food habits should be flexible
enough to vary with a new situation.

 

I like for my family to stick to the old
favorite meals, rather than mess them up
with new and different kinds of foods.

 

Unfamiliar foods interest me.

 

If I am satisfied with foods I eat, I see no
reason for me to change.

 

I believe that the person who gets the most
satisfaction out of eating is the one who
sticks to the foods that are familiar.

 

Trying new and different foods appeals to me.

 

I would be willing to spend time in making
nutritious foods available for myself and/or
family instead of eating convenience
foods of low nutritional quality.

 

I would be willing to try an unfamiliar food

 

 

 

 

at least once. i

g

 

 

Have you had computer experience previous to the computerized diet analysis assignment?

a. yes
b. no

Did you have any problems getting to a computer to do the diet analysis assignment?
a yes (explain in II 57)
b. no

Did you have any problems running the diet analysis program?
a. yes (explain in If 57)
b. no

Did you review any information in the 'Main Nutrient Menu/Current Nutrition lssues'

section?
a. yes
b. no

Were you able to understand the computer printout of your diet analysis?

a. yes
b. no (explain in t 57)

 

51.

52.

55.

56.

57.

103

How would you rate this assignment in terms of helping you apply the concepts taught in
HNF 102?

very useful

somewhat useful

neutral

not very useful

not useful at all

9999'!"

Do you feel this exercise wll be helpful to others in learning nutrition?
a. yes
b. no

Did you use the program other than the two assigned times?
a. yes

What is your overall GPA?
a. 1.5 - 1.9

2.0 - 2.4

2.5 - 2.9

3.0 - 3.4

3.5 - 4.0

9'

99.0

Would you like to see the program available on campus for your use in the future?
a. yes
b. no

Comments/suggestions:

104

HNF 102
Sec. 3
Bond (F, 88)

HNF 102 POSTTEST

Fill in your name, student number and section number on the answer sheet.

Read the following questions and choose the best answer for the multiple choice questions. For
questions 2345, select the answer that reflects your degree of agreement with each
statement. THIS POSTTEST WILL IN NO WAY AFFECT YOUR GRADE FOR THE

COURSE.

1. The correct match to the name of the B vitamin is
a. B1-thiamin
b. BZ-pyridoxine
c. BS-niacin
d. 81 2-riboflavin
e. B6ocobalamin

2. Which of the following is produced by bacteria in the intestinal tract?
vitamin A

vitamin K

ascorbic acid

vitamin E

9.0.62

3. A deficiency of iodine may be seen as
a. anemia
b. spoon-shaped finger nails
c. goiter
d. skin rashes

4. Absorption rate of iron
a. is increased when the body’s need for iron is Increased

b. is approximately 50%
c. in the heme form is lower than in iron from plant sources
(I. in the normal diet is so high that it reduces absorption of cepper

5. The stored form of carbohydrates in the body is called
a. glycogen
b. insulin
c. fat
d. muscle

6. Proteins are essential in the diet

as the major source of energy .
to provide nitrogen for growth and tissue development

to furnish the only source of hydrogen
to provide essential fatty acids

P-S’P’P

105

7. Elevated concentrations of this Iipoprotein are associated with a decreased risk of
cardiovascular disease

a. low density lipoprotein

b. high density lipoprotein

c. lecithin

d. linoleic acid

8. The major form of fat in the diet is
a. cholesterol
b. lecithin
c. triglyceride
d. polyunsaturated fat

9. Of the following, which is the best source of polyunsaturated fat?
soybean all

animal fat

margarine

shortening

coconut oil

.09-99’."

10. The correct function of the organ in the digestive tract is
a. the mouth is the major site of carbohydrate digestion
b. the small intestine is chiefly a storage reservoir
c. the stomach is the site where most digestion occurs
d. the pancreas secretes amylase and lipase into the small intestine

11. The specific dynamic action reflects the increase in energy expenditure
a. caused by respiration
b. caused by circulation
c. caused by glandular activity
d. following ingestion of food

12. Normally the most energy is expended for
a. Basal Metabolic Rate (BMR)
b. physical activity
c. thermogenesis
d. digestion of food

13. Pica is
a. a vitamin deficiency
b. craving for non-foods such as clay, laundry starch, sand and
newspaper
0. most commonly seen in men and adolescent boys
d. a recommended mineral supplement at the level of the RDA

14. Which of the following is considered a nutrient?
a. carbohydrate
b. lipids
0. water
d. all of the above

106

15. Your breakfast included a fried egg, whole wheat toast, and orange juice. Which Basic Four
Food Group was not represented?

16.

17.

18.

19.

a. meat group

b. dairy group

c. grain group

d. fruit and vegetable group

During pregnancy
a. the increased need for energy exceeds the increased need for protein
and minerals
b. two extra servings of iron-enriched bread are sufficient
to meet the increased iron needs
c. the mother needs to double the amount of food she eats by
the sixth month to meet the baby's needs
d. it is recommended that the average mother gain approximately 25-30 pounds

The requirements for water based on 1000 kcals expended is the greatest for
a. infants
b. adults
c. teenagers
d. elderly

The Recommended Dietary Allowances for fat and carbohydrate
a. have not been established
b. are the same for both
c. depend on ideal body weight
d. are based on basal metabolic rate

Which food group has the highest percent of complex carbohydrate?
a. milk group
b. meat and alternates group
c. fruit and vegetables group
d. cereal and grains group
e limited extras

20. The general guidelines in nutrition recommend what percent of your calorie intake should
be from fat?

21.

a. 15%
b. 20%
c. 30%
d. 40%
e. 55%

The adult recommendation from the Basic Four Food Plan is
4 servings each day from the dairy group

2 servings each day from the cereal group

2 servings each day from the fruit and vegetable group
2 servings each day from the meat group

P-PF’P

22.

23.

24.

25.

26.

27.

28.

29.

30.

31.

32.

35.

36.

37.

107

If you consumed 100 grams of fat and your total caloric intake
percent of your energy intake was from fat?

a. 5%
b. 20%
c. 22%
d. 50%

I believe that diet is a major controlling
factor when it comes to maintaining health
and controlling disease.

I feel the foods I eat now will affect my
future health.

I am concerned about eating nutritious
foods throughout the day.

Nutrition is extremely important during pregnancy.

I feel that I need to learn more about
good nutrition.

If a person is not eating well his/her health
will surely suffer.

I feel that as long as I am maintaining my
weight, I don't have to worry much about
nutrition.

As long as the doctor doesn't say much to
me about nutrition, I don't think I need to
worry about it.

I feel that all college students need to
take at least one course in nutrition.

I feel that if I drink milk I don't have
to worry about nutrition.

Nutrition is important and one should not
be careless about it

Even if I take vitamins, I feel that I should
be concerned about the foods I eat.

I would fix more nutritious meals if I knew
what to prepare.

For better health, I would be willing to try
a food I hadn‘t eaten before or several foods
over a period of time.

Exploring several methods of food preparation
is desirable.

was 1800 kcals, what

 

I‘ U 3 r r?—
512: M: 3 8 7ft:
£351 "(708’ ‘5’) ~51
4‘” Q51 v.3 $5

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

39.

40.

41.

42.

43.

45.

108

I think that food habits should be flexible
enough to vary with a new situation.

I like for my family to stick to the old
favorite meals, rather than mess them up
with new and different kinds of foods.

Unfamiliar foods interest me.

If I am satisfied with foods I eat, I see no
reason for me to change.

I believe that the person who gets the most
satisfaction out of eating is the one who
sticks to the foods that are familiar.

Trying new and different foods appeals to me.

I would be willing to spend time in making
nutritious feeds available for myself and/or
family instead of eating convenience
foods of low nutritional quality.

I would be willing to try an unfamiliar food
at least once.

 

Z' 79
fly ,0 .5 2 7w
3n. 't' 09 4"?)
MM: 2% 333310

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

APPENDIX D

109

Appendix D
HNF 102
Sec.2
Bond (F 88)
ONE-DAY DIET RECORD

ASSIGNMENT (Part I)

2 pts. for Part I. Part II, also 2 pts will be assigned later in the quarter. You must
complete both parts to recieve 4 pts. for the assignment.

This assignment is intended to increase your awareness of the adequacy of your diet.
Take this sheet home with you. Select a WEEKDAY (not Saturday or Sunday) and record
all the food and drink you consume for that day. List foods as completely and accurately
as possible. You will NOT be graded on the adequacy of your diet.

Be sure to include:

a) beverages: water, milk, soft drinks, juice, tea, coffee,
alcoholic beverages, etc.
b) condiments: butter, margarine, mayonnaise, catsup,
mustard, pickle relish, cream, sugar, jelly, sauces, etc.
0) method of preparation: fried, baked, boiled, broiled, etc.
d) anything added during preparation: oil, milk, wine, etc.
9) for combination foods, list all ingredients as accurately as
possible

Be sure to note estimated quantity of food. Describe portion sizes by ounces, cups,
tablespoons, etc. For example, rather than "1 glass of milk“, estimate ounces as close as

possible.

1 cup - 8 ounces (fluid)
1 tablespoon = 3 teaspoons
1/4 pound .. 4 ounces (weight)

Turn in your diet record sheet and accompanying demographic and activitydata sheet to
the instructor or teaching assistants before or after class to renew credit for the

assignment.

Graduate assistants will use a computerized diet analysis program to assess. your diet.
The computer printout of your diet analysis will be available for you later inthe quarter

(date will be announced).

110
HNF 102
Sec.2
Bond (F 88)

Please fill this sheet out completely and turn it in with your one-day diet record. This
information is necessary to calculate your diet analysis.

DATE:

STUDENT NUMBER:
SEC

ACE:

HEIGHT: FT. IN.
WEIGHT: LB.

HAVE YOU BEEN TAKING ANY VITAMIN OR MINERAL SUPPLEMENTS?
(YES OR NO)

ARE YOU PREGNANT OR LACTATING? (YES OR NO)
RECORD THE AMOUNT OF TIME (IN HOURS) THAT IS SPENT IN EACH OF THE ACTIVITIES

UST ED BELOW: (IT IS EASIEST TO RECORD HOURS SLEEPING AND THEN WORK UP FROM
THE LAST CATEGORY)

SLEEPING ...................................................................................................... h r.
UGHT ACTIVITY
(EX. STUDYING, TYPING, COOKING, DRIVING, SITTING, WATCHING T.V.).. h r.
MODERATE ACTIVITY
(EX. WALKING, BICYCLING, DANCING, TENNIS, SWIMMING) ................. hr.
HEAW ACTIVITY
(EX. BASKET BALL, AEROBICS, RUNNING, CROSS-COUNT RY SKIING, ROWING) h r.
TOTAL:

(24 hrs)

NAME:

111

ONE-DAY DIET RECORD _

I.D. #
if!

 

ITEM AND DESCRIPTION

PORTION

 

Meal 1

 

Snack(s)

 

Meal 2

 

Snack(s)

 

Meal 3

 

Snack(s)

 

 

 

Vitamin/Mineral Supplement(s)

Is this a complete one-day Intake?

Was this a TYPICAL day?

 

Yes

__ Yes
No

 

No

 

 

112

HNF 102
Sec.3
Bond (F 88)

ONE-DAY DIET RECORD
ASSIGNMENT (PART I)

2 pts. for Part I. Part II, also 2 pts. will be assigned later in the quarter. You must
complete both parts to receive 4 pts. for the assignment.

This assignment is Intended to Increase your awareness of the adequacy of your diet.
Take this sheet home with you. Select a WEEKDAY (not Saturday or Sunday) and record
all the food and drink you consume for that day. List foods as completely and accurately
as possible. You will NOT be graded on the adequacy of your diet.

Be sure to include:

a) beverages: water, milk, soft drinks, juice, tea, coffee,
alcoholic beverages. etc.

b) condiments: butter, margarine, mayonnaise, catsup, mustard,
pickle relish, cream, sugar, jelly, sauces, etc.

0) method of preparation: fried, baked, boiled, broiled, etc.

(I) anything added during preparation: oil, milk, wine, etc.

6) for combination foods, list all Ingredients as accurately as
possible

Be sure to note estimated quantity of food. Describe portion sizes by ounces, cups.
tablespoons, etc. For example, rather than "1 glass of milk“, estimate ounces as close as
possible. .

1 cup - 8 ounces (fluid)
1 tablespoon =- 3 teaspoons
1/4 pound - 4 ounces (weight)

After you have completed your diet record, you will run a computerized diet analysis
program for assessment of your diet. Bring your diet record sheet with you to one of the
computer labs listed below. Hours will be reserved for HNF 102 students at the times
listed. and open hours are also available. The computerized diet analysis program 'MSU
NutriGuide', will be available from the assistants In the Human Ecology lab and the
Student Union lab. Bessey Hall computer labs will have the program on the mainframe,
but you will need to get a bootdisk and NutriGuide A disk from the monitor in Rm. 210.
Follow the attached instructions for the lab you are attending and proceed through the
program. The computer will automatically print 2 copies of your analysis. Turn one
copy in to the instructor or teaching assistants before or after class, and keep the second
copy for your own use.

NOTE: YOU ARE FREE TO USE NUTRIGUIDE AT ANY TIME DURING THE QUARTER!
The program will be available for checkout, and you are encouraged to practice
using the program for assessment of your diet.

113

ONE-DAY DIET necono _

NAME: l.D. #

##

 

 

ITEM AND DESCRIPTION PORTION

 

Meal 1

 

Snack(s)

 

Meal 2

 

Snack(s)

 

Meal 3

 

Snack(s)

 

 

 

 

Vitamin/Mineral Supplement(s)

 

Is this a complete one-day Intake? __ Yes No
Was this a TYPICAL day? Yes No

 

APPENDIX E

114

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APPENDIX F

119

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APPENDIX G

122

Kraemer and Thiemann AE’Lowerix Analysis: (p. 42)

Equation for the two-sample t-test

GS" dtmrmcc in means you consider impcrimn'l"

 

c’
_ {>53 _ .. .
A (‘3; 4.11.3}; . where A critical effect snze
P‘t.
n-v+2 where p+q-1: proportion p of the total n subjects

from one group and proportion q of the
total n subjects from a second group.
For my study p=.70, q-.30

(112_- 70. ZL- 30)
254 254

Sample equation: Iron intake for females

Significant difference in means - 6mg (1/3 of the RDA: I arbitrarily
chose this)

0’:- 7.5 (from my data)

-5_= 0.8
§ 7.5

[3' .
48+:7>1<—33='
A-..

2.32

A -.34

Using the tables at the back of the book, look up At“. 5% level,
two-tailed test, 80% power (p.110):

 

 

 

v - 64, n - 64+2 - 55_ subjects

I assume that my sample size is large enough to be confident in my
results because I have 201 subjects.

APPENDIX H

123
Appendix H

SPSS-X Data Program

TITLE 'THESIS DATA ANALYSIS'
FILE HANDLE SHERRY/NAMEs'SHERRY DATA1 A'
DATA LIST FILE-SHERRY FIXED RECORDS=34 NOTABLES
1/IDNUMBER 1-7 LAST NAME 9-18 (A) PRESCORE 19-21 KNOWB1 TO
KNOWB23 22-43 ATTB1 TO ATT823 44-66 RESIDE 67 LEVEL 68
ENROLL 69
2/POSTSCOR 19-21 KNOWA1 TO KNOWA23 22-43 ATTA1 TO
ATTA23 44-66 COMPUTE1 TO COMPUTE10 67-77
3/GENDER 29(A) AGE 31-32 HEIGHT 34-38 WEIGHT 40-44 ENEEDS 46-
50 VITAMIN 52 PREG 64 SLEEP 56-59 LIGHT 61-64 MOD 66-69
HEAW 71-74
4/CALORIE 9-14 CRDA 15-20 CSNACK 21-25
PROTEIN 27-32 PRDA 33-38 PSNACK 39-43
CARBO 45-50 CARDA 51-56 CASNACK 57-61
FAT 63-68 FRDA 69-74 FSNACK 75-79
5/POLYFAT 9-14 PFRDA 15-20 PFSNACK 21 -25
SATFAT 27-32 SFRDA 33-38 SFSNACK 39-43
CHOL 45-50 CHRDA 51-56 CHSNACK 57—61
ALCOHOL 63-68 ALRDA 69-74 ALSNACK 75-79
6/VITA 9-14 ARDA 15-20 ASNACK 21-25
VITD 27-32 DRDA 33-38 DSNACK 39-43
VITE 45-50 ERDA 51-56 ESNACK 57-61
B1 63-68 B1RDA 69-74 B1SNACK 75-79
7/82 9-14 82RDA 15-20 B2SNACK 21-25
NIACIN 27-32 NlARDA 33-38 NIASNACK 39-43
86 45-50 BGRDA 51-56 eesNACK 57-61
812 63-68 B12RDA 69-74 B12 SNACK 75-79
8/FOLACIN 9-14 FOLRDA 15-20 FOLSNACK 21-25
VlTC 27-32 VCRDA 33-38 VCSNACK 39-43
IRON 45-50 FERDA 51-56 FESNACK 57-61
CALCIUM 63-68 CALFIDA 69-74 CALSNACK 75-79
9/PHOSPHOR 9-14 PHOSRDA 15-20 PHOSNACK 21-25
SODIUM 27-32 NARDA 33-38 NASNCAK 39-43
POTASS 45-50 POTRDA 51-56 POTSNACK 57-61
MAGNES 63-68 MAGRDA 69-74 MAGSNACK 75-79
10/ZlNC 9-14 ZNRDA 15-20 ZNSNACK 21-25
DFIBER 27-32 DFRDA 33-38 DFSNACK 39-43
CAFFEINE 45-50 CAFRDA 51-56 CAFSNACK 57-61
11/FOOD1 9-14 MEAL1 16 SVG1 18-20

124

F0002 21 -26 MEAL2 28 SVG2 30-32
F0003 33-38 MEAL3 40 SVGS 42-44
F0004 45-50 MEAL4 52 SVG4 54-56
F0005 57-62 MEALS 64 SVGS 66-68
F0006 69-74 MEAL6 76 SVGB 78-80
12/F0007 9-14 MEAL7 16 SVG7 18-20
F0008 21 -26 MEAL8 28 SVGB 30-32
F0009 33-38 MEALS 40 SVGQ 42-44
F00010 45-50 MEAL10 52 SVG10 54-56
F00011 57-62 MEAL11 64 SVG11 66-68
F00012 69-74 MEAL12 76 SVG12 78-80
13/F00013 9-14 MEAL13 16 SVG13 18-20
F00014 21-26 MEAL14 28 SVG14 30-32
F00015 33-38 MEAL15 40 SVG15 42-44
F00016 45-50 MEAL16 52 SVG16 54-56
F00017 57-62 MEAL17 64 SVG17 66-68
F00018 69-74 MEAL18 76 SVG18 78-80
14/F00019 9-14 MEAL19 16 SVG19 18-20
F00020 21 -26 MEAL20 28 SVGZO 30-32
F00021 33-38 MEAL21 40 SVG21 42-44
F00022 45-50 MEAL22 52 SVG22 54-56
F00023 57-62 MEAL23 64 SVG23 66-68
F00024 69-74 MEAL24 76 SVGZ4 78-80
15/F00025 9-14 MEAL25 16 SVGZS 18-20
F00026 21-26 MEAL26 28 SVGZG 30-32
F00027 33-38 MEAL27 40 SVGZ7 42-44
F00028 45-50 MEAL28 52 SVGZ8 54-56
F00029 57-62 MEAL29 64 SVGZQ 66-68
F00030 69-74 MEAL30 76 SVG30 78-80
16/F00031 9-14 MEAL31 16 SVG31 18-20
F00032 21-26 MEAL32 28 SVGSZ 30-32
F00033 33-38 MEAL33 40 SVG33 42-44
F00034 45-50 MEAL34 52 SVGS4 54-56
F00035 57-62 MEAL35 64 SVGSS 66-68
F00036 69-74 MEAL36 76 SVGS6 78-80
17IF00037 9-14 MEAL37 16 SVG37 18-20
F00038 21-26 MEAL38 28 SVG38 30-32
F00039 33-38 MEAL39 40 SVGSQ 42-44
F00040 45-50 MEAL40 52 SVG40 54-56
F00041 57-62 MEAL41 64 SVG41 66-68
F00042 69-74 MEAL42 76 SVG42 78-80
18/F00043 9-14 MEAL43 16 SVG43 18-20
F00044 21-26 MEAL44 28 SVG44 30-32

125

F00045 33-38 MEAL45 40 SVG45 42-44
F00046 45-50 MEAL46 52 SVG46 54-56
F00047 57-62 MEAL47 64 SVG47 66-68
19/PGENDER 29 PAGE 31-32 PHEIGHT 34-38 PWEIGHT 40-44
PENEEDS 46-50 PVITAMIN 52 PPREG 54 PSLEEP 56-59 PLIGHT61-
64 PMOD 66-69 PHEAVY 71 ~74
20/PCALORIE 9-14 KCRDA 15-20 KCSNACK 21-25
PROT 27-32 PRO0A33-38 PRONAK 39-43
CH045-50 CHORDA 51-56 CHOSNAK 57-61
TFAT 63-68 TFRDA 69-74 TFSNAK 75-79
21/POLFAT 9-14 PLFRDA 15-20 PLFSNAK 21-25
SFAT 27-32 STFRDA 33-38 STFSNAK 39-43
CLST 45-50 CLRDA 51-56 CLSNAK 57-61
ALCO 63-68 ALCRDA 69-74 ALCSNAK 75-79
22/VlTAMA 9-14 VARDA 15-20 VASNAK 21-25
VITAMD 27-32 VDRDA 33-38 VDSNAK 39-43
VITAME 45-50 VERDA 51-56 VESNAK 57-61
THIA 63-68 THRDA 69-74 THSNAK 75-79
23/RIBOF 9-14 RIBRDA 15-20 RIBSNAK 21-25
NIAC 27-32 NIRDA 33-38 NISNAK 39-43
PYRIDOX 45-50 PYRRDA 51-56 PYRSNAK 57-61
VITB12 63-68 12ROA 69-74 128NAK 75-79
24/FOLIC 9-14 FORDA 15-20 FOSNAK 21-25
VITAMC 27-32 VICRDA 33-38 VICSNAK 39-43
IRONB 45-50 IRRDA 51 ~56 IRSNAK 57-61
CALC 63-68 CCMRDA 69-74 CCMSNAK 75-79
25/PHOS 9-14 PHORDA 15-20 PHOSNAK 21-25
800 27-32 SODRDA 33-38 SODSNAK 39-43
POT 45-50 KRDA 51 -56 KSNAK 57-61
MAGNE 63-68 MGRDA 69-74 MGSNAK 75-79
26/ZIN 9-14 ZINRDA 15-20 ZINSNAK 21-25
FIBER 27-32 DFIRDA 33-38 DFISNAK 39-43
CAFF 45-50 CFFRDA 51 -56 CFFSNAK 57-61
27/F000A1 9-14 MEALA1 16 SVG18 18-20
F000A2 21-26 MEALB1 28 SVG2B 30-32
F000A3 33-38 MEALC1 40 SVGSB 42-44
F000A4 45-50 MEAL01 52 SVG4B 54-56
F000A5 57-62 MEALE1 64 SVGSB 66-68
F000A6 69-74 MEALF1 76 SVG6B 78-80
28/F000A7 9-14 MEALG1 16 SVG7B 18-20
F000A8 21-26 MEALH1 28 SVGBB 30-32
F000A9 33-38 MEALI1 40 SVGSB 42-44
F000A10 45-50 MEALJ1 52 SVG1OB 54-56

126

F000A11 57-62 MEALK1 64 SVG11B 66-68
F000A12 69-74 MEALL1 76 SVG12B 78-80
29/F000A13 9-14 MEALM1 16 SVG138 18-20
F000A14 21 -26 MEALN1 28 SVG14B 30-32
F000A15 33-38 MEAL01 40 SVG1SB 42-44
F000A16 45-50 MEALP1 52 SVG16B 54-56
F000A17 57-62 MEALQ1 64 SVG17B 66-68
F000A18 69-74 MEALR1 76 SVG188 78-80
30/F000A19 9-14 MEALS1 16 SVG198 18-20
F000A20 21-26 MEALT1 28 SVGZOB 30-32
F000A21 33-38 MEALU1 40 SVGZ1B 42-44
F000A22 45-50 MEALV1 52 SVGZZB 54-56
F000A23 57-62 MEALW1 64 SVGZSB 66-68
F000A24 69-74 MEALX1 76 SVGZ4B 78-80
31/F000A25 9-14 MEALY1 16 SVGZSB 18-20
F000A26 21 -26 MEAL21 28 SVG268 30-32
F000A27 33-38 MEALAA1 40 SVG27B 42-44
F000A28 45-50 MEALBB1 52 SVGZBB 54-56
F000A29 57-62 MEALCC1 64 SVGZQB 66-68
F000A30 69-74 MEAL001 76 SVG3OB 78-80
32/F000A31 9-14 MEALEE1 16 SVG31B 18-20
F000A32 21-26 MEALFF1 28 SVGSZB 30-32
F000A33 33-38 MEALGG1 40 SVGSSB 42-44
F000A34 45-50 MEALHH1 52 SVG34B 54-56
F000A35 57-62 MEALII1 64 SVGSSB 66-68
F000A36 69-74 MEALJJ1 76 SVGSGB 78-80
33/F000A37 9-14 MEALKK1 16 SVGS7B 18-20
F000A38 21 -26 MEALLL1 28 SVGSBB 30-32
F000A39 33-38 MEALMM1 40 SVG3QB 42-44
F000A40 45-50 MEALNN1 52 SVG4OB 54-56
F000A41 57-62 MEAL001 64 SVG41B 66-68
F000A42 69-74 MEALPP1 76 SVG42B 78-80
34/F000A43 9-14 MEALQQ1 16 SVG43B 18-20
F000A44 21 -26 MEALRR1 28 SVG44B 30-32
F000A45 33-38 MEALSS1 40 SVG4SB 42-44
F000A46 45-50 MEALTT1 52 SVG4SB 54-56
F000A47 57-62 MEALUU1 64 SVG47B 66-68
F000A48 69-74 MEALVV1 76 SVG48B 78-80

UST 0F REFERENCES

UST 0F REFERENCES

Ajzen,l. and Fishbein, M.: Understanding Attitudes and Predicting
Social Behavir. Englewood Cliffs: Prentice-Hall, 4-26, 1980.

Allen, J.Fl.: Current trends in computer-assisted instruction.
Computers in the Humanities 7: 47-55, 1972.

Amstutz, MK. and Dixon, D.L.: Dietary changes resulting from the
Expanded Food and Nutrition Program. J Nutr Ed 18: 55-69, 1986.

Basiotis, P.P., Welsh, 8.0., Cronin, F.J., Kelsey, J.L., and Mertz, W.:
Number of days of food intake records required to estimate
individual and group nutrient intakes with defined confidence. J of
Nutr 117: 1638-1641, 1987.

Beaton, G.H., Milner, J., Corey, P., McGuire, V., Cousins, M., Stewart,
E., deRamos, M., Hewitt, D., Grambsch, V., and Little, J.A.: Sources of
variance in 24-hour dietary recall data: implications for nutrition
study design and interpretation. Am J Clin Nutr 32: 2546-2559,
1979.

Beerman, K.A., Jennings, G., and Crawford, 3.: The effect of student
residence on food choice. J Amer Coll Health 38: 215-220, 1990.

Berkowitz, AD. and Perkins, H.W.: Problem drinking among college
students: A review of recent research. J Amer Coll Health 35: 21 -
28, 1986.

Berkowitz, AD. and Perkins, H.W.: Recent research on gender
differences in collegiate alcohol use. J Amer Coll Health 36: 123-
129, 1987.

Bitzer, D.L. and Alpert, 0.: Advances in computer-based education,
Science 167: 1582-1590, 1970.

127

128

Boren, A.R., Dixon, PM, and Reed, 0.8.: Measuring nutrition attitudes
among university students. J Am Diet Assoc 82: 251-253, 1983.

Brown, E.L.: College students look at the basis for their habits. J
Home Econ 59: 784, 1967.

Brush, K.H., Woolcott, D.M., and Kawash, G.F.: Evaluation of an
affective-based adult nutrition education program. J Nutr Ed 18:
258-264, 1986.

Byrd-Bredbenner, C., Shannon, 8., Hsu, L., and Holderness-Smith D.: A
nutrition education curriculum for senior high home economics

students: Its effect on students' knowledge, attitudes, and behaviors.
J of Nutr Educ 20: 341-346, 1988.

Carew, L.B., Elvin, D.W., Yon, BA, and Alster, PA: A college-level,
computer-assisted course in nutrition. J Nutr Educ 16: 46-50, 1984.

Carruth, B.R., Mangel, M., and Anderson, H.L.: Assessing Change- .
proneness and nutrition-related behaviors. J Am Diet Assoc 70: 47-

53. 1977.

Chalmers, F.W., Clayton, M.M., Gates, L.O., Tucker, R.E., Werts, A.W.,
Young, CM, and Foster, W.D.: The dietary record - how many and
which days? J Am Diet Assoc 28: 711-717, 1952.

Conklin, D.N.: A study of computer-assisted instruction in nursing
education. J Computer-Based Instr 9: 98-107, 1983.

Coughenour, M.C.: Functional aspects of food consumption activity
and family life cycle stages. J Marriage Family 34: 656, 1972.

Crocker,L. and Algina,J.: Introduction To Classical and Modern Test
Theory. New York: CBS College Publishing, 1986.

129

Davis, J.E. and Reynolds, N.C.: Alcohol use among college students:
Responses to raising the purchase age. J Amer Coll Health 38: 263-
269, 1990.

Deardorff, W.W.: Computerized health education: a comparison with
traditional formats. Health Educ Q 13: 61-72, 1986.

Dence, M.: Toward defining the role of CAI: a review. Educ Tech 20:
50-54, 1980.

Dickens, 0.: Factors related to food preference. J Home Econ 57: 427,
1965.

Douglas, PD. and Douglas, J.G.: Nutrition knowledge and food
practices of high school athletes. J Am Diet Assoc 84: 1198-1202,

1984.

Eppright, E.S., Fox, H.M., Fryer, B.A., Lamkin, G.H., and Vivian, V.M.: The
North Central Regional Study of diets of preschool children. Part 2. .
Nutrition knowledge and attitudes of mothers. J Home Econ 62: 327-

332, 1970.

Fanelli, MT and Stevenhagen, K.J.: Consistency of energy and
nutrient intakes of older adults: 24-hour recall vs. 1-day food
record. J Am Diet Assoc 86: 665-667, 1986.

Foley, C.S., Vaden, A.G., Newell, G.K., and Dayton, A.D.: Establishing
the need for nutrition education: III. Elementary students' nutrition
knowledge, attitudes and practices. J Am Diet Assoc 83: 564-568,

1983.

Gersovitz, M., Madden, JP, and Smiciklas-Wright, H.: Validity of the
24-hr dietary recall and Seven—day record for group comparIsons. J

Am Diet Assoc 73: 48-55, 1978.

130

Gillespie, A.H.: Research example 2. Communication theory as a basis
for nutrition education. J Am Diet Assoc 87 (Suppl): S-44, 1987.

Gold, RS and Duncan, D.F.: Computers and health education. J School
Health 50: 503-505, 1980.

Gottschalk, P.L., Macaulay, C.M., Sawyer, J.M., and Miles, J.E.: Nutrient
intakes of university students living in residence. J Can Diet Assoc
38: 47, 1977.

Greger, J.L. and Etnyre, G.M.: Validity of 24-hour dietary recalls by
adolescent females. Am J Pub Health 68: 70-72, 1978.

Grimm, F.M.: Computer simulations as a teaching tool in community
college. Amer Biology Teacher 40: 362-364, 1978.

Grotkowski, ML. and Sims, L.S.: Nutritional knowledge, attitudes, and
practices of the elderly. J Am Diet Assoc 72: 499-506, 1978.

Guiry, V.C. and Bisogni, C.A.: Caffeine knowledge, attitudes, and
practices of young women. J Nutr Ed 18: 16-22, 1986.

Guthrie, H.A.: Selection and quantification of typical food portions by
young adults. J Am Diet Assoc 84: 1440-1444, 1984.

Guthrie, HA. and Crocetti, A.F.: Variability of nutrient intake over a
3-day period. J Am Diet Assoc 85: 325-327, 1985.

Guthrie, HA. and Scheer, J.C.: Validity of a dietary score for
assessing nutrient adequacy. J Am Diet Assoc 78: 240-245, 1981.

Hall, J.C.: A method for the rapid assessment of sample size in
dietary studies. Am J Clin Nutr 37: 473-477, 1983.

Hansen, R.G. and Wyse, B.W.: Expression of nutrient allowances per
1,000 kilocalories. J Am Diet Assoc 76: 223-227, 1980.

131

Haseba, J., and Brown, M.L.: Breakfast habits of college students in

Hawaii. J Am Diet Assoc 53: 334, 1968.

Hernon, J.F., Skinner, J.D. Andrews, FE. and Penfield, M.P.: Nutrient

intakes and foods selected by college students: Comparisons among
subgroups divided by energy intake. J Am Diet Assoc 86: 217-221,
1986.

Hoffman, C.J.: Dietary intake of calcium, iron, folacin, alcohol, and
fat for college students in central Michigan. J Am Diet Assoc 89:
836-838, 1989.

Holden, P.M.: How advertising affects food habits. Food and Nutr
Notes Rev. 28: 102, 1971.

Jakobovits, C., Halstead, P., Kelley, L., Roe, DA, and Young, C.M.:
Eating habits and nutrient intakes of college women over a thirty-
year period. J Am Diet Assoc 71: 405, 1977.

Jalso, S.B., Burns, MM, and Rivers, J.M.: Nutrition beliefs and
practices. J Am Diet Assoc 47: 263-268, 1965.

Jenkins, RM. and Guthrie, H.A.: Identification of index nutrients for
dietary assessment. J Nutr Ed 16: 15-18, 1984.

Karvetti, R. and Knuts, L.: Validity of the 24-hour dietary recall. J
Am Diet Assoc 85:1437-1442, 1985.

Khan, MA. and Lipke, L.K.: Snacking and its contribution to food and
nutrient intake of college students. J Am Diet Assoc 81: 583-587,

1982.

Koch, W.J.: Basic facts about using the computer in Instruction. Educ
Digest 38: 28-31, 1973.

132

Kraemer, HO. and Thiemann, 8.: How Many Subjects? Statistical
Power Analysis In Research. California: Sage Publications, 1988.

Krebs-Smith, SM. and Clark, L.D.: Validation of a nutrient adequacy
score for use with women and children. J Am Diet Assoc 89: 775-
783, 1989.

Kulik, J.A., Kulik, CC, and Cohen, P.A.: Effectiveness of computer-
based college teaching: a meta-analysis of findings. Review of Educ
Research 50: 525-544, 1980.

Kulvahy, W.K.: Feedback and response confidence. J of Educ Psych 68:
522-528, 1976.

Lewellen, L.: Computers in the classroom. J of Educ Data Processing
8: 33-38. 1971.

Linusson, E.E.l., Sanjur, D. and Erickson, E.C.: Validating the 24-hour
recall method as a dietary survey tool. Arch Latinoam Nutr 24: 277-.

293, 1974.

Lowenburg, M.E., Todhunter, E.N., Wilson, E.D., Savage, JR, and
Lubawski, J.L.: Food and Man. 2nd Ed. New York: John Wiley and Sons,

1974.

Madden, J.P., Goodman, SJ, and Guthrie, H.A.: Validity of the 24-hr
recall. J Am Diet Assoc 68: 143-147, 1976.

Magidson, E.M.: One more time: CAI is not dehumanizing. AV lnstruc
22: 20-21, 1977.

Magidson, E.M.: Trends in computer-assisted instruction. Educ
Technology 18: 5-63, 1978.

133

Mathis, A., Smith, T., and Hansen, D.: College students's attitudes
toward computer-assisted instruction. J of Educ Psych 61: 46-51,
1970.

Molnar, A.R.: Computer Innovations in Education. Washington, DC:
National Science Foundation, 1972.

Morgan, R.W., Jain, M., Miller, A.B., Choi, N.W., Matthews, V., Munan, L.,
Burch, J.D., Feather, J., Howe, GR, and Kelly, A.: A comparison of
dietary methods in epidemiologic studies. Am J Epidem 107: 488-
497, 1978.

Newell, G.K., Vaden, A.G., Aitken, ER, and Dayton, A.D.: Food
consumption and quality of diets of Kansas elementary students. J
Am Diet Assoc 85: 939-945, 1985.

Okey, JR. and Mayer, K.: Individual and small-group learning with
computer-assisted instruction. AV Comm Rev 24: 79-86, 1976.

O'Leary, L., and Lee, M.: Nutrient intakes of university women. J Can
Diet Assoc 36: 53, 1975.

Ostrom, S. and Labuza, T.P.: Analysis of a seven-day diet survey of
college students. Food Tech May: 68-76, 1977.

Pao, E.M., Mickle, SJ, and Burk, M.C.: One-day and 3-day nutrient
intakes by individuals - Nationwide Food Consumption Survey
findings, Spring 1977. J Am Diet Assoc 85: 313-324, 1985.

Pennington, J.T.: Dietary Nutrient Guide. Westport, CT. AVI
Publishing Co. pp. 40-44, 1976.

Perron, M., and Endres, J.: Knowledge, attitudes and dietary practices
of female athletes. J Am Diet Assoc 85: 573-576, 1985.

134

Pumpian-Mindlan, E.: The meaning of food. J Am Diet Assoc 30: 576,
1954.

Ries, CF. and Granell, J.: CAI in college-level nutrition education: a
feasibility study. J Nutr Ed 17: 130-134, 1985.

Rosander, K. and Sims, L.: Measuring effects of an affective-based
nutrition education intervention. J Nutr Ed 13: 102-105, 1981.

Ross, J.K.: Nutrition attitudes and knowledge of nursing students. J
Am Diet Assoc 84: 687-688, 1984.

Sarvela, P.D. Taylor, E.L., Drolet, J.C., and Newcomb, P.R.: Indicators
of drinking and driving among university students. Health Educ Oct-
Nov: 72-77, 1988.

Schroeder, L. and Kent, P.: Computer-based instruction in dietetics
education. J Computer-Based Instr 8: 85-90, 1982.

Schwartz, N.: Nutrition knowledge, attitudes and practices of high
school graduates. J Am Diet Assoc 66: 28-31, 1975.

Sheperd, R. and Stockley, M.: Nutrition knowledge, attitudes, and fat
consumption. J Am Diet Assoc 87: 615-619, 1987.

Soliah, L.L., Newell, G.K., Vaden, A.G., and Dayton, A.D.: Establishing
the need for nutrition education: ll. Elementary teachers' nutritIon
knowledge, attitudes, and practices. J Am Diet Assoc 83: 447-453,

1983.

Sorenson, A.W., Wyse, B.W., Wittwer, A.J., and Hansen, R.G.: An index
of nutritional quality for a balanced diet. J Am Diet Assoc 68:236-

242, 1976.

Statistical Package for the Social Siences (SPSS-X): SPSS Inc.,
Chicago, IL., 1988.

135

St. Jeor, S.T., Guthrie, HA, and Jones, M.B.: Variability in nutrient
intake in a 28-day period. J Am Diet Assoc 83: 155-162, 1983.

Stasch, A.R., Johnson, MM, and Spangler, G.J.: Food practices and
preferences of some college students. J Am Diet Assoc 57: 523,
1970.

Stuff, J.E., Garza, C., Smith, E.O., Nichols, BL, and Montandon, C.M.: A
comparison of dietary methods in nutritional studies. Am J Clin
Nutr 37: 300-306, 1983.

Suppes, P. and Morningstar, M.: Computer-assisted instruction.
Science 66: 343-350, 1969.

Swanson, J.C.: Second thoughts on knowledge and attitude effects
upon behavior. J School Health 42: 363-365, 1972.

Tait, K., Hartley, JR, and Anderson, R.C.: Feedback procedures in
computer-assisted arithmetic instruction. J of Educ Psych 43: 161 -.
171, 1974.

Todd, K.S., Hudes, M., and Galloway, D.H.: Food intake measurement:
problems and approaches. Am J Clin Nutr 37: 139-146, 1983.

Valois, R.F.: Student drug use and driving: A university sample.
Health Educ Feb-March: 38-42, 1986.

Vinsonhaler, JP. and Bass, R.K.: A summary of ten major studies on
CAI drill and practice. Educ Tech 12: 29-32, 1972.

Waddell, K.P. and Rinke, W.J.: Effectiveness of a computer-assisted
instruction program for teaching sanitation. J Am Diet Assoc 85:

62-67, 1985.

Wade, CW. and Thiele, V.: Computer assisted instruction in a college
nutrition course. J Nutr Educ 5: 246-248, 1973.

136

Wakefield, L.M., and Miller, M.C.: Several factors affecting college
coeds' food preferences, habits, and intake. J Home Econ 63: 45,
1971.

Weinberg, AD. and Scott, L.W.: The use of the computer in education:
focusing on nutrition. Health Values: Achieving High Level Wellness
7: 22-28, 1983.

Werblow, J.A., Fox, HM, and Henneman, A.: Nutritional knowledge,
attitudes, and food patterns of women athletes. J Am Diet Assoc 73:
242-245, 1978.

Wilhelmy, 0., Young, C.M., and Pilcher, H.L.: Nutritional status survey,
Groton Township, New York. 3. Nutrient usage as related to certain
social and economic factors. J Am Diet Assoc 26: 868, 1950.

Worthington-Roberts, B., Little, RE, Lambert, MD, and Wu, R.:
Dietary cravings and aversions in the postpartum period. J Am Diet
Assoc 89: 647-651, 1989.

Young, C.M., Berresford, K., and Waldner, 8.6.: What the homemaker
knows about nutrition. 3. Relation of knowledge to practice. J Am

Diet Assoc 32: 321, 1956.

Young, C.M., Hagan, G.CT, Tucker, RE, and Foster, W.D.: A comparison
of dietary study methods. J Am Diet Assoc 28: 218-221, 1952.

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