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This is to certify that the
dissertation entitled

VALIDITY AND RELIABILITY OF STAGE OF CHANGE INSTRUMENTS
AND PROCESSES OF CHANGE TO EAT FRUITS AND VEGETABLES

presented by

Sang-din Chung

has been accepted towards fulﬁllment
of the requirements for

 

Ph.D. degree in Human Nutrition

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VALID

VALIDITY AND RELIABILITY OF STAGE OF CHANGE INSTRUMENTS AND
PROCESSES OF CHANGE TO EAT FRUITS AND VEGETABLES

By

Sang-J in Chung

A DISSERTATION
Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
Department of Food Science and Human Nutrition

2001

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ABSTRACT

VALIDITY AND RELIABILITY OF STAGE OF CHANGE INSTRUMENTS AND
PROCESSES OF CHANGE TO EAT FRUITS AND VEGETABLES

By

Sang-Jin Chung

The purposes of this study were: 1) to establish validity and reliability of staging
instruments for eating adequate servings of fruits and vegetables; 2) to identify processes
of change for eating adequate servings of fruits and vegetables; and 3) to ﬁnd factors
associated with inadequate servings of fruits and vegetables. Food intake and
psychometric data were obtained from a convenience sample of 294 college students:
80% female and 86% white. To establish outcome validity of several methods used to
assign stage of readiness to eat adequate fruits or adequate vegetables, servings from a 3-
day food record were calculated. The methods differed only by how fruit and vegetable
information was collected, i.e., self-rated intake; a 24-hour recall; or food frequencies of
fruits and vegetables for the past week. The criteria for validating post-action stages in
all methods were at least 2 servings of fruits or 3 servings of vegetables from a 3-day
food record. Average fruit and vegetable servings by all methods distinguished pre- from
post-action stages. For fruits, however, the 24-hour was concluded recall showed a
higher agreement with the criteria (Cohen’s K=0.54, p <0.05)), had good reliability and
the highest sensitivity compared to the other two methods. For vegetables, all methods
showed only marginal agreement (Cohen’s K<0.40, p<0.05). A 24-hour recall was
concluded to accurately assess an individual’s stage of change in eating fruits, but further

research is necessary to develop a good way of assessing vegetable intakes.

terms:
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By adapting processes of change items from previous studies, 29 items for seven
change processes for eating ﬁ'uits and vegetables (Health Concerns, Self Reevaluation,
Social Liberation, Health Commitment/Action, Interpersonal Control, External
Reinforcement and Helping Relationships) were developed using conﬁrmatory factor
analysis. When subjects’ uses of these change processes were compared to their stages of
readiness to change for ﬁuit, use of self-reevaluation differed from health
commitment/action. For vegetables, use of health commitment, health concern and self-
reevaluation processes differed among stages. Health commitment/action along with
self-reevaluation of intakes appeared to be important processes used to eat enough fruits
and vegetables. The process of health concerns was associated only with eating enough
vegetables.

For college students, when less than 2 servings of fruit and less than 3 servings of
vegetables were used to indicate inadequate intakes from a 3-day food record, 58% and
82% of respondents reported inadequate intakes of total fruit and fruit without juice, 0
respectively. Fiﬁy-three and 63% reported inadequate intakes of total vegetables and
vegetables without fried potatoes, respectively. Inadequate fruit consumption was less
prevalent in females, university housing residents, non-smokers, regular exercisers and
regular breakfast eaters. Self-efﬁcacy was inversely associated with inadequate intakes
of both fruits and vegetables. Inadequate ﬁ'uit intake was positively" associated with
higher discretionary fat intakes, but inadequate vegetable intake was not. Therefore,
eating fruit was more associated with other positive health behaviors than was eating
vegetables. When fruit juice and fried potatoes were excluded, fruit and vegetable

intakes were positively associated with each other.

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ACKNOWLEDGMENTS

I would like to express my sincerest appreciation to my adviser, Professor Sharon L.
Hoerr, for her guidance with patience, encouragement and invaluable help throughout the
years of my graduate study at the Michigan State University. I also want to extend a very
special thanks to Dr. Won 0. Song for her supportive advice and Dr. Wanda L
Chenoweth and Dr. Stephen Yelon for serving on my defense committee. I also
appreciate Dr. Ralph Levine’s help for statistical advice about factor analysis. All my
accomplishments, especially this dissertation, are the results of my parents’ unconditional
love and support. Finally, I would like to thank my husband, Seung-ho Kang, who
always gives love, ﬁ'iendship and statistical advice, and my daughter, Minji, who brings

me joy in life. Without their love and patience, I would not been able to ﬁnish this work.

 

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Validity

TABLE OF CONTENTS

LIST OF TABLES ................................................................................. vii
LIST OF FIGURES ................................................................................. ix
CHAPTER 1
INTRODUCTION ................................................................................... 1
CHAPTER 2
REVIEW OF RELATED LITERATURE ....................................................... 4
Fruit and vegetable consumption in US. .................................................... 4
Young adults as sample population ......................................................... 8
Assessment and current consumption of Fruit and Vegetable in the US ................ 8
Factors Related to Eating Fruits and Vegetables ......................................... 15
Transtheoretical Model ...................................................................... 19
Three dimensional model ........................................................ 19
Stage of Change-1"t dimension ................................................. 20
Process of Change-2“d dimension .............................................. 24
Decisional balance-part of 3rd dimension ..................................... 27
Self-efﬁcacy-part of 3rd dimension ............................................. 29
Validity of Stage of Change assessment tool ............................................. 30
Types of validity .................................................................. 3O
Criterion validity issues for Stage of Change assessment tool and
dietarybehavior ........................... 31
Other efforts to establish criterion validity in dietary behavior ............ 33
Studies of construct validity ..................................................... 3 5
Reliability of Stage of Change assessment tool .......................................... 36
Direction for developing Stage of Change assessment tool for dietary behavior ....38
CHAPTER 3
VALIDITY OF STAGES OF CHANGE INSTRUMENTS FOR EATING FRUITS AND
VEGETABLES ...................................................................................... 40
A. Abstract ...................................................................................... 40
B. Introduction .................. ‘ ............................................................... 41
C. Methods ...................................................................................... 43
. D. Results ....................................................................................... 47
E. Discussion .................................................................................... 48
F. Application .................................................................................. 52
CHAPTER 4
DEVELOPING AN INSTRUMENT TO MEASURE THE PROCESSES YOUNG
ADULTS USE FOR EATING FRUITS AND VEGETABLES ............................. 59
A. Abstract ........................... , ......................................................... 59
B. Introduction ................................................................................ 6O

C. Methods .................................................................................... 63

D. Results ........................................................ - .............................. 68
E. Discussion .................................................................................. 69
F. Implication for research and practice .................................................... 74
CHAPTER 5
SELF-EFFICACY AND HEALTH BEHAVIORS ARE ASSOCIATED WITH YOUNG
ADULTS’ FRUIT AND VEGETABLE CONSUMPTION .................................... 81
A Abstract ..................................................................................... 81 .
B. Introduction ........................................................ - ......................... 82
C. Methods ..................................................................................... 85
D. Results ....................................................................................... 88
E. Discussion ................................................................................... 91
CHAPTER 6
REPORT OF CONSTRUCT VALIDITY BY THREE STAGING METHODS ........... 99
A. Results ............................................................... . ...................... 99
B. Discussion. ................................................................................ 101
CHAPTER 7
CONCLUSIONS AND RECOMMENDATIONS ........................................... 109
APPENDICES
A. UCRIHS APPROVAL ................................................................... 116
B. INFORMED CONSENT ................................................................ 118
C. FOCUS GROUP INTERVIEW PROTOCOL & INSTRUMENT
(STEP 1) ..................................................................................... 123
D. QUESTIONNAIRES FOR REFINING ITEMS OF PROCESS OF CHANGE
(STEP 2) .................................................................................... 141

E. STAGING INSTRUMENTS A (SELF RATED F/V INTAKES), B (24 HR
RECALL) ,& C ( F/v FOOD FREQUENCY FOR F/V), DEMOGRAPHICS,
FINAL PROCESS OF CHANGE, DECISIONAL BALANCE, SELF EFFICACY

& 3 DAY FOOD RECORDS (STEP 3) ................................................ 150

BIBLIOGRAPHY ................................................................................ 174

LIST OF TABLES

CHAPTER 2

Table 1. Recent studies of fruit andvegetable consumption in the US ...................... 7
Table 2. Recent studies comparing assessment tools for fruit and vegetable intake ...... 14
Table 3. Factors related to increased intakes of hits and vegetables ..................... 18
Table 4. Dimensions of Stages of Change Model ............................................. 21
Table 5. Types of processes of change .......................................................... 22
Table 6. Stages of dietary change: Algorithm and items ..................................... 25
CHAPTER 3

Table 1. Percentage distribution of respondents by stages of change for eating fruits and
vegetables using three staging methods and servings of fruit and vegetable

[Mean(SD)] ﬁom 3 day food records .................................................. 55
CHAPTER 4
Table 1. Factor correlation and reliability of each construct ................................. 76

Table 2. Average T-scores (Standard Deviation) of the seven processes of change for

stages of change by self-rated fruit and vegetable intake .......................... 80
CHAPTER 5 '
Table 1. Percentage of participants by demographics and health behaviors ................ 95

Table 2. Average of psychosocial factors, food group intakes and frequency
' of breakfast eating by gender (Mean j; Standard deviation) ....................... 96

Table 3. Odds Ratio and 95% conﬁdence intervals1 for inadequate consumption
of fruits and of vegetables by demographic and personal factors after
adjusting energy intake .................................................................. 97

Table 4. Odds Ratio and 95% conﬁdence intervals1 for inadequate consumption
of hits and of vegetables by psychosocial variables and other food groups ....98

vii

CHAPTER 6
Table]. Average T-scores [Mean(SD)] of decisional balance and self-efﬁcacy
by stages of staging Methods A,B & C ............................................... 104

Table 2. Average T-scores [(Mean(SD)] of the seven processes of change by stages of
staging Methods A, B & C .............................................................. 106

viii

LIST OF FIGURES

CHAPTER 3
Figure 1. Method A (Self rated intake) of classifying the stages of change ................ 53

Figure 2. Method B (24hr recall) and Method C (FF Q) for classifying the stages of
Change ............................................................ q ........................ 54

Figure 3. Agreement for detecting intake from three staging methods compared to 3 day
food record using behavioral criteria .................................................. 56

Figure 4. Ability of three staging methods to detect low and adequate huh and vegetable

intake, Sensitivity & Speciﬁcity, respectively ...................................... 57
Figure 5. Test-Retest Reliability of Stage agreements by 3 staging methods .............. 58
CHAPTER 4
Figure 1. Stages of Change algorithm for eating fruits and vegetables ....................... 78

Figure 2. Comparison of processes of change for eating vegetable - Relapser vs
Pre-action -R ............................................................................ 79

I Figure 3. Comparison of processes of change for eating vegetable
Maintenance by habit vs Maintenance by change .................................. 79

ix

Chapter One

Introduction

Adequate intake of fruits and vegetables, including beans, is important for many
essential vitamins, minerals, dietary ﬁber and to reduce risks for chronic disease (Block
et al., 1992; Ness and Powles, 1997; Appel et al., 1997; Pillow et al., 1997; Freudenheim
et a1. 1996). People in the US. have low intakes of fruits and vegetables (Subar et al.,
1995). For these reasons, various public policies have been set to increase intakes of
fruits and of vegetables such as the Food Guide Pyramid, Dietary Guidelines, Healthy
People 2010 and Five-A—Day (USDA & USDHHS, 1992; USDA & USDHHS, 1990;
National Research Council, 1989; US. Department of Health and Human Services, 2000;
Subar et al., 1995).

Establishing sound dietary habits in young adulthood has been shown to be
important for good health in later adulthood (Lau et al., 1990; Hampl and Betts, 1995). If
people establish good habits while young, it is easier to maintain these good behaviors
than to change later. Because young adults’ fruit and vegetable intakes have been
reported as low (Georgiou et al., 1997), targeting dietary intervention to this age group
should be cost effective in the long term (U .S. Department of Health and Human
’ Services, 2000).

Stages of Change Theory, a TranstheoretiCal theory which integrates concepts and
techniques from many different behavioral theories (Prochaska, 1979; Prochaska et al.,
1992a; Glanz et al., 1994), has been a successful model used to change smoking and drug

abuse behaviors (Prochaska et al., 1992a; DiClemente et al., 1982). Therefore, it

encourages nutrition educators to become interested in applying Stages of Change Theory
to dietary habits. However, eating habits differ from smoking or drug abuse behaviors in
complexity, deﬁnition and subject recognition of the behaviors involved. Although
Stages of Change Theory has potential as a useful behavioral model based on the ﬁndings
of a linear relationship in intake of fat across the stages ﬁ'om Precontemplation to
Maintenance (Greene et al., 1994; Sporny and Contento, 1995; Hoerr et al., 1997), stage
assessment based on self-reported dietary intake has often failed to show validity in terms

of behavioral criterion of achieving the dietary goal. Thus, Stages of Change Theory may
misplace people into inaccurate stages, likely because people were unaware of whether
they were eating the recommended amount of food or nutrient (Brug et al., 1997; Glanz
et al., 1994; Sporny and Contento, 1995). Because the Stages of Change Theory in
dietary intervention has shown some promise of effectiveness via tailored intervention
messages (Campbell et al., 1994), correctly identifying a person’s stage or readiness to
change dietary behavior is needed to appropriately target interventions.

Prior to speciﬁc dietary interventions, a ﬁrst priority is developing a valid and
reliable Stage of Change instrument to satisfy the behavioral criterion of achieving the
dietary goal and of understanding the processes of change behavior for intakes of fruits
and vegetables. Therefore, the objectives of this study were:

1) To develop valid and reliable Stages of Change staging instrument(s) for eating the
recommended number of servings of fruits and of vegetables based on actual intakes,
decisional balance for making the change and self-efficacy for fruit and vegetable intake

by college age young adults (Chapters Three & Six);

2) To identify processes of change for eating at least 2 servings of fruit and 3 servings
of vegetables and the different use of processes among stages (Chapter Four);

3) To identify relationships between actual fruit and vegetable intake and related factors
such as psychosocial factors (self—efﬁcacy, temptation, decisional balance), other food

group intake and demographic factors in this population (Chapter Five).

Chapter Two

Review of Literature

Eating diets rich in fruits and vegetables has been a public policy focus due to its
association with a decreased risk for chronic diseases such as heart disease, colon cancer,
lung cancer and breast canoer (Block et al., 1992; Ness and Powles, 1997; Appel et al.,
1997; Pillow et al., 1997; Freudenheim et a1 1996; Djuric et al., 1998; Kant et al., 1992).
One of the Year 2010 Health Objectives for the United States is to increase the intake of
fruits and vegetables to ﬁve or more servings per day (National Research Council, 1989;
US. Dept of Health and Human Services, 2000). Many studieshave been published
related to intake of fruits and vegetables and associated psychosocial factors and health
effects. This literature review relates to the current intake of fruits and vegetables in the
US. and studies about increasing fruit and vegetable intake include those examining the
psychosocial factors for eating fruits and vegetables. Stage of Change Theory is
described and studies related to its use with food reported. A short discussion on validity
and reliability as related to assessment tools for Stages of Change to increase fruits and
vegetables concludes this chapter. Finally, within each subsection the research is

evaluated in terms of how ﬁndings relate to this proposed study.

Fruit and vegetable consumption in the US.
Many studies showed that most people in the US. have low intakes of fruits and
vegetables. Dietary data from 8181 adults (>20 yr old) in the USDA’s 1989-1991

Continuing Surveys of Food Intakes by Individuals (CSFII) over 3 days, using a one-day

24-hr recall and two-day food records, showed 1.2 mean servings of fruits consumed and
3.1 mean servings of vegetables. Adults’ vegetable intake relied heavily on potatoes (1.0
servings per day), including french fries (0.4 servings). Although the absolute number of
servings of fruits and vegetables were higher for men than for women, women consumed
more servings per 1000 calorie diet than men (2.8 vs 2.3 servings). Average total intakes
rose by age and income. Only 32% of adults met the objective of ﬁve or more servings
of fruits and vegetables per day (Krebs-Smith et al., 1995a).

Surveys of3148 children and adolescents in 1989-1991 CFSII data showed only
20% ate more than 5 servings of fruits and vegetables (Krebs-Smith et al., 1996).
Although intakes of fruits and vegetables slightly increased in 1994-1995 CF 811
compared to 1989—1991, the national objective of S-A-Day was still not met (Enns et al.,
1997)

The median intake of fruits and vegetables from a baseline assessment for the S-A-
Day in the summer of 1991 was 3.4 servings per day. The Center for Disease Control
estimated this number from a frequency checklist of intake of 33 fruits and vegetables.
This survey was done on a nationally representative adult sample (n=2811; 48% response
rate) by random digit dialing. Increased years of education, income and nonsmoking
status were important predictors of increased fruit and vegetable intakes. - Women
showed higher intakes of both fruits and vegetables than men at all ages (Subar et al.,
1995)

The results of the Behavioral Risk Factor Survey in 16 states (n=23,699; 82%
response) showed a median of 3.5 daily servings of all fruits and vegetables. Direct

questions with sub-categories like, “How often do you eat green salad?” were used.

Young adults 18-24 years of age reported the lowest median intake of fruits and
vegetables, 2.8 servings for men and 3.0 servings for women (Serdula et al., 1995).

i A study focused on young adults (18-24 years of age) in a random mail survey in 9
states (n=1338; 43% response) showed 1.4-1.6 servings of fruits and 1.7-1.9 servings of
vegetables per day as median intakes (Georgiou et al., 1997). College students were at
the high end of the ranges, and non-students were at the lowest.

The average fruits and vegetables intake, including french fries, from 2-day intakes
for Expanded Food and Nutrition Education Program women in two counties in
Michigan, 1996, was 1.0112 servings of fruits and 2.7115 servings of vegetables (Hoerr '
et al., 1997). The mean from 2-day intakes of adolescent mothers (mean age 21 years)
has been reported also to be low, with 0.8 servings of fruits and 2.2 servings of
vegetables, including ﬁench fries (Hoerr et al., 1998).

Fruit and vegetable intake by 2- or 3-day food records averaged 4.4 servings in
both the general US. population and for Expanded Food and Nutrition Education
Program women (Hoerr et al., 1997; Chung and Hoerr, 1998). Median intakes of fruits
and vegetables reported in various studies appeared to be about 3.5 servings (See
summary in Table 1). Only 20-30% of the people in several studies ate 5 servings of
ﬁuits and vegetables, combined. If 2 servings of fruits and 3 servings of vegetables are
set as the minimum objective, then the percentage of people who meet this goal will be
even less. Therefore, increasing fruit and vegetable intake should be a goal for the entire

.U. S. population.

Table 1. Recent studies of fruit and vegetable consumption in the U.S.

 

 

 

Study Subjects Instruments Method to count Results
FV .

Krebs-Smith et al., 8,181 >20 yr 1 d recall + Food Grouping Mean: 1.2 F,
1995a 1989-91 2 d recall system by USDA 3.1 V

CF SII (calculate 32% ate _>_5FV

ingredients)

Serdula et al., 1995 23,699 adults Questions Total FV in each Median: 3.5 FV

in 16 states w/ sub- category, 20% ate ZS FV

BRFSS categories excluding ﬂied

like juice, potato
salad
Subar et al., 1995 2,811 adults, 33 item FV Excluded ﬂied Median: 3.4 FV
_ 1991 FFQ potato 23% ate :5 FV

Krebs-Smith et al., 3,148 youth 1 d recall + Food Grouping Mean: 1.2 F,
1996 1989-91 2 (1 record system by USDA 2.4 V

CFSH 20% ate ZSFV
Georgiou et al., 1,338 young 60 item Median:
1997 adult, l8-24yr FFQ 1.4-1.6F

} 1.7-1.9 V
Hoerr et al., 1997 EFNEP 1 d recall Calculate Mean: 1.0 F,
‘ women ~28yr +1 d record ingredients 2.7 V

Hoerr et al., 1998 Adolescent 1 d recall Calculate Mean: 0.8 F,

Mothers +1 (1 record ingredients 2.2 V

‘~21yrs
F=ﬂuit, V=vegetab1e
FF Q= Food Frequency Questionnaire

BRFSS= Behavioral Risk Factor Surveillance System
'CSFII= Continuing Surveys of Food Intakes by Individuals

Young adults as sample population

Early practice of sound dietary habits in young adulthood is associated with
reduced risk for chronic disease later in life (Raitakari et al., 1994). Establishing good
habits early makes it easier to maintain good behaviors as an adult rather than to have to
change later (Lau et al., 1990). Therefore, targeting dietary change intervention during
this time should be cost effective in the long term (U .S. Department of Health and
Human Services, 2000).

People’s dietary habits do not change easily, but changing behavior is possible
even though behavior typically changes slowly (Giﬁt et al., 1972). Furtherrnore, while
preadolescent children likely do not have the cognitive development to have concerns
about future health risks (Domel and Baranowski, 1995), young adults have the necessary
mental processing equipment to do so, at least biologically. Young adults in college are
usually also in a transitional period between living and eating at home and living on their
own and feeding themselves (Lau et al., 1990).

Unfortunately, young adults’ ﬂuit and vegetable intakes have been reported to be
low althOugh college students and graduates have more healthﬁrl habits than nonstudents
(Georgiou et al., 1997). The determinant factors of young adults for what they eat are
reported as time and convenience, health concerns and money (Betts et al., 1995).

However, habit is also an important factor (Betts et al., 1997).

Assessment and current consumption of fruits and vegetables in the U.S.

Measuring ﬂuit and vegetable intake accurately is necessary to assess

consumption. However, validation of actual ﬂuit and vegetable intake is difficult,

because there is no “gold standar ” or criterion method for ﬁnding the true and usual
dietary consumption in’ populations. Self-estimation of ﬂuit and vegetable intake often
appears inaccurate compared to self-reported food records ﬂom which nutrition
professionals calculate the ﬂuit and vegetable servings (Smith-Warner et al., 1997;
Chung and Hoerr, 1998). (See summary in Table 2.)

Self-rated ﬂuit and vegetable intakes generally are higher than the self-reported
food records, recalls or food frequency questionnaires. A food frequency questionnaire
given to a Dutch adult population via telephone interview (n=3 67) was used to assess
objectively the consumption of fruits, salads and processed vegetables using an 8-item
food ﬂequency (Lechner et al., 1997). Subjective estimation of ﬂuit and vegetable intake
was assessed by asking subjects to rate their own intakes of ﬂuits, salads and processed
vegetables with a 5-point scale ﬂom very low to very high. Eighty-eight percent of the
respondents who did not eat enough vegetables (<150 grams per day) answered that they
ate enough vegetables; and 65% of the respondents who had low ﬂuit intake (< 2 pieces
per day) reported themselves to eat enough. Another study with EFNEP women using 2-
day food records compared to a self-rated, one item food ﬂequency question, showed
these women overestimated by 0.7 serving of fruits and underestimated by. 0.4 serving of
vegetables per day (Hoerr et al., 1997). A study with a college population comparing
ﬂuit and vegetable consumption between a 7-item food ﬂequency and a 2-day diet record
also showed a 0.3 serving per day overestimation of ﬂuit intake and 0.4 serving per day
underestimation of vegetable intake by food ﬂequency (Plesko et al., 2000). In a study of
similar comparisons with parenting young moms, subjects underestimated 1 serving per

day of fruit and vegetable intake combined (Chung et al., 1998).

Three food checklists or short ﬂequency questionnaires used in three national
surveys were compared and mean servings reported (Krebs-Smith et al., 19950). The 20
questions for ﬂuits and vegetables in the 1987 National Health Interview Survey showed
23.8 times per week as a median ﬂequency of intake (3.4 servings/day). The median
intake was 34.6 (4.9 servings/day) using the 33 questions in the 5-A-Day for Better
Health Program. The 40 questions in the NHANES I Epidemiologic Follow-up Survey
showed 38.8 (5.5 servings/day) as a median intake. Researchers concluded that
estimating the total ﬂequency by summing up individual foods ﬂom checklists might not
be valid for fruit and vegetable intake, because the larger number of ﬂequency questions
about ﬂuits and vegetables appeared to increase the estimation of total fruit and vegetable
intake (Krebs-Smith et al., 1995c). A study in which ﬁndings supported this conclusion
was done in the United Kingdom. In this cohort study, women (3 5-69 years of age)
reported a higher intake of ﬂuits and vegetables ﬂom 19 ﬂuits (excluding dried ﬂuits and
fruit juice) and 31 vegetables (excluding potatoes) on a food ﬂequency questionnaire
compared to a simple cross-check question (Calvert et al., 1997). An example of a
cross-check question is, “How many servings of ﬂuits and fruit containing dishes do you
eat per week?” Eighty-one percent of respondents had overestimated their ﬂuit intake
compared to the cross-check questions and 93% of the respondents overestimated
vegetable intake. Some survey researchers suggest that ﬂequency questionnaires or
checklists with many items lead to higher estimates of food consumption than do food
records or recalls (Block, 1982; Feskanich et al., 1993).

In Minnesota three dietary assessment methods were compared ﬂom 201

participants (3 0-74 years of age) diagnosed with colorectal adenomas. Investigators used

10

15 days of diet records (ﬁve 3-day records at 3 month intervals), two l-month and one 1-
year food ﬂequency questionnaires with 59 ﬁuit and vegetable items, and six question
modules with sub-categories for the estimation of fruit and vegetable intake used in the
Behavioral Risk Factor Surveillance System. The number of servings of ﬂuit and
vegetable intake, excluding ﬂied potatoes, showed similar results between the records
and ﬂequencies, 6.3 and 6.5, respectively. However, different results ﬂom the 6-item
module, 3.8 servings, was reported (smith-Warner et al., 1997). In this study,
reproducibility between the baseline and 3 months of each assessment was reported using
the Pearson correlation coefﬁcient. The correlation for 3-day diet records was r=0.52
which increased to 0.82 after correction for the ratio of within- to between person
variability. Correlations of r=0.70 for the 1-month food ﬂequency and 0.49 for the 6-
item module were reported.

Another study examined the six questions for fruits and vegetables ﬂom the
Behavioral Risk Factor Surveillance System compared to multiple diet records or recalls
or food frequency in several separate studies of various U.S. regions: Wisconsin,
Chicago, Arizona, and Georgia. Results showed similar mean intakes between the six
questions’and multiple food records or recalls except for an overestimation of fruit and
vegetable intakes by the six questions in Arizona (Serdula et al., 1993). However, the
intake estimations by the six questions were lower than those estimated ﬂom 29 to 40
ﬂuit and vegetable item food ﬂequencies. In this study, total ﬂuit and vegetable intake
excluded ﬂied potatoes, ﬁuit pastries and dried beans. Total ﬂuit and vegetable intake
was 2.1-4.0/day using six questions and 2.1-4.3/day using multiple food records or recall

and 3.6-5.6/day using food ﬂequencies.

11

Classiﬁcation of foods as ﬂuits and vegetables and estimating serving size are
some of the important factors affecting validity for estimating people’s intake. One study
compared three methods to count ﬂuit and vegetable intake with 24—hour dietary recalls
in 617 fourth-grade students. Different results were obtained ﬂom different counting
methods (Eldridge et al., 1998). Students average 3 .9 servings by the S-A-Day method,
4.1 servings by the U.S. Food and Drug Administration Reference amounts and 5.1
servings by the Minnesota Cancer Prevention Research Unit Method. All these methods
excluded ﬂied potatoes from FV intake. The S-A-Day method did not include pickled
ﬂuits and vegetables or soy products. The University of Minnesota Method counted 1/2
cup of ﬂuit instead of 1 medium ﬂuit as 1 serving of ﬂuit. The amount of one serving of
each food in the U.S. Food and Drug Administration Method is described using grams,
not by using cups or individual units of food. Investigators recommended choosing the
best method to ﬁt the purpose of the study when counting ﬂuit and vegetable intake.

Self-rated ﬂuit and vegetable intake ﬂom one direct question or several questions
tends to overestimate intakes compared to those counts ﬂom short food ﬂequencies.
Whereas ﬂuit and vegetable intakes were underestimated by self-rated questions when
compared to more detailed food ﬂequencies, when self-rated ﬂuit and vegetable intake
was compared to dietary records or recalls, the results were inconsiStent. Healthy normal
adult populations tended to report similar intakes or overestimate ﬂuit and vegetable
intakes, and populations with disease or young moms tended to underestimate fruit and
vegetable intakes. Most researches showed similar results in ﬂuit and vegetable intake
between food ﬂequencies and food records,iexcept for one study which reported 1

serving more of ﬂuit and vegetable intake ﬂom food ﬂequency than food records.

12

Subjects’ ability to deﬁne foods as ﬂuit and vegetable can also affect the number of
servings of ﬂuits and vegetables reported. One study using .153 female elementary
school teachers showed the number of days’ records necessary as the gold standard to get
reliable fruit and vegetable intakes. Five weekdays of food records were necessary to
achieve 0.80 intraclass correlation reliability and 3 weekdays of food records were
necessary to get 0.70 reliability for ﬂuit and vegetable intake (Baranowski et al., 1997).

Most studies have compared the average ﬂuit and vegetable intakes or reported the
correlation coefﬁcient between assessment methods, but a correlation is not necessarily
the best way to examine which assessment method detects people with adequate and
inadequate ﬂuit and vegetable intakes most accurately. For this dissertation research, the
evaluation methods for detecting adequate versus inadequate intakes such as Cohen’s K,
sensitivity and speciﬁcity will be used to compare three assessment methods: self-rated,
24-hour recall and food ﬂequency using average ﬂuit and vegetable servings ﬂom 3-day
intakes as the gold standard. The criterion for adequate fruit and vegetable intakes will
be at least 2 servings for fruits and 3 for vegetables, instead of the combined total of 5

ﬂuits and vegetables.

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14

Factors related to eating fruits and vegetables

There have been several studies about determinants or psychosocial factors related
to eating fruits and vegetables. Although habitual behaviors have been reported to be less
affected by self-efficacy, attitude, knowledge and social inﬂuence (Triandis, 1977),
generally, knowledge of food selection, belief in diet-disease relationships and good
attitudes toward dietary change goals have had positive associations with high fruit and
vegetable intakes (Smith et al., 1995; Patterson et al., 1995). (See summary in
Table 3.) V

Cancer-prevention knowledge and perceived ease of eating a healthful diet were
strong predictors of intake for 10,286 U.S. adults aged 18 years and older in the 1992
National Health Interview Survey Cancer Epidemiology Supplement (Harnack et al.,
1997). A study of attitudes toward fruit and vegetable consumption in a W1C population
(>19yr old, 48% Aﬁ'ican American) showed positive perceptions of fruits and vegetables
were important to intakes. However, low income women in the study also reported
barriers to increase consumption such as lack of availability, time and effort to prepare,
and preference for other foods (Treiman et al., 1996). Another study reported that the
nutrition behavior scores of randomly sampled Washington state residents were largely
dependent on the barriers to fruit and vegetable intake. In the Washington study,
elements of the Health Belief Model - including beneﬁts of and barriers to fruit and
vegetable intake, susceptibility to cancer and nutrition concerns - explained 16% of the
variance of FV intake behaviors (Dittus et al., 1995).

The Minnesota Adolescent Health Survey, completed by 36,284 adolescents in

grades 7-12 using simple, direct questions about fruit and vegetable intakes, reported that

15

 

adolescents with low socioecdnomic status were twice as likely to eat inadequate fruits
and 1.5 times more likely to eat inadequate vegetables than those of middle income
parents (N eumark-Sztainer et al., 1996). African-Americans were at lower risk for
inadequate fruit intake with an 0.73 of odds ratio (OR) and at higher risk of inadequate
vegetable intake (OR : 1.73), compared to Whites (p<0.001). Approximately 40% of
adolescents from low socioeconomic backgrounds reported less than one serving a day of
fruits or vegetables. Native American youth were at highest risk for inadequate fruit
intake. Psychosocial factors related to inadequate intake of fruits and vegetables were
low family connectedness (OR: 2.1., p<0.001), weight dissatisfaction (OR: 1.3, p<0.001)
and poor academic achievement (OR: 1.6, p<0.001). Frequent dieting was associated
with inadequate vegetable intake (OR: 1.3, p<0.001), but not with fruit intake in this
population (N eumark-Sztainer et al.,.1996).

Psychosocial factors related to fruit and vegetable intakes have been also reported
in other studies. Knowledge about recommended servings of fruits and vegetables in the
S-A-Day Baseline Survey was reported as the most important determinant of actual fruit
and vegetable intake (Krebs-Smith et al., 1995b). Self-efﬁcacy and positive attitudes in a
study of Dutch adults were signiﬁcantly associated with consumption of cooked
vegetables, of salads or of fruits. Social inﬂuence was signiﬁcantly associated with only
, salad consumption, but not with consumption of boiled vegetables or fruits (Brug et al.,
1995). Another study with Dutch adults showed that for eating salads, attitude, social
inﬂuence, self-efﬁcacy and intention were important predictors in both fruit and
vegetable intakes measured both subjectively and objectively (Lechner et al., 1997).

Self-efﬁcacy and intention were important predictors for fruit and processed vegetable

l6

intake measured objectively, and attitude was important for fruit intake measured
subjectively. Attitude and social inﬂuence were important for processed vegetable
intake. Other research with 407 adults by a random-digit dial telephone survey in Rhode
Island showed that respondents with children at home were at greater risk for eating 2 or
fewer servings of ﬁuits and vegetables a day than those without children at home
(Laforge et al., 1994). In another study of 1398 3rd grade children, food preferences and
positive outcome expectations were signiﬁcantly associated with ﬁuit and vegetable
intake obtained by a 7-day food record (Resnicow et al., 1997).

In this dissertation research, the association of inadequate fruit and vegetable
intakes with demographics, other health behaviors, other food intakes and psychosocial
factors will be examined to identify the important factors for eating hits and vegetables

for collegiate young adults in order to develop the eﬁ‘ective interventions.

17

 

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18

Transtheoretical Model

Efforts to improve food intake must be on a theoreticalimodel for behavioral
change in order to be effective (Glanz et al., 1994). In this section the Transtheoretical
Model which has shown recent promise for dietary change is described and recent
research evaluated.

Three dimensional model

The Transtheoretical Model has a central organizing construct, Stage of Change.
The model also includes a set of intervening or dependent measures, which are the pros
and cons for the behavior from Decisional Balance, Self-efﬁcacy and Temptation, and a
set of independent variables, including the processes of change. Researchers have
described the Transtheoretical Model as three dimensional for: l) the Stages of Change;
2) the processes of change; 3) the decisional balance, self-efﬁcacy and temptation, and
outcome behaviors specific to the problem (Prochaska and DiClemente, 1984b). (See
Table 4.)

The Stages of Change, the ﬁrst dimension, represents the temporal, motivational,
and constancy aspects of change (DiClemente and Prochaska, 1985). The second
dimension, called processes of change, focuses on activities and events to create
successful modiﬁcation of a problem behavior. The ten processes of change from
smoking cessation and twelve processes of change from Weight control (Table 5)
represent coping activities (Prochaska et al., 1988; DiClemente et al., 1991; Prochaska et
al., 1992). The third dimension includes decisional balance, self-efﬁcacy, temptation and
the outcome behavior (Martin et al., 1996). Most researches to date have focused

primarily on a single construct of the model, the stage and outcome behavior. Some

19

researchers are now also including the decisional balance, self-efﬁcacy and temptation
constructs (Brug et al., 1997; Prochaska et al., 1994, Betts et al., abstract). The processes
of change are the least studied aspect of the Stages of Change model, especially for
dietary behaviors.

More research is clearly needed on the entire model, instead of just a focus on the
Stages of Change in isolation ﬁ’om the other dimensions especially as related to
intervention (Prochaska and Velicer, 1997 a).

Stage of Change-l8t dimension

The Transtheoretical model of Stages of Change theory of behavior change was
formulated to understand and inﬂuence how people change health behaviors and
originated to explain smoking cessation (DiClemente et al., 1991; Prochaska and
DiClemente, 1983). Stages of Change theory has been tested with several problem
behaviors (Prochaska et al., 1994). The assumption is made for the Stages of Change
theory that people recognize their own intentions to change a speciﬁc health behavior and
that this is a necessary step to assign people to pre-action stages: Precontemplation
(unaware, no intention to change); Contemplation (thinking about change); and
Preparation (making plans to change behavior in the near future or have made some
changes but have not reached a particular criterion). Likewise, people must be able to
recognize the time period within which they are making current health changes in order
for health practitioners to determine those pe0ple in post-action stages. Post-Action
stages include Action, actively changing behavior, and Maintenance, maintaining desired
behavior. These assumptions of the Stages of Change Theory are made for all problem

behaviors, including those related to diet (Prochaska and Velicer, 1997a).

20

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21

Table 5. Types of processes of change (Prochaska et al, 1992)

 

Experiential

 

Consciousness raising

Increasing information about self and problem

 

Self-reevaluation

Assessing how one feels and thinks about oneself with
respect to a problem

 

Dramatic relief

Experiencing and expressing feeling about one’s
problems and solution

 

Environmental
reevaluation

Assessing how one’s problems affect personal and
physical environment

 

Social-liberation

Increasing alternatives for non-problem behaviors
available in society

 

 

 

Behavioral

Self-liberation Choosing and making a commitment to act or belief in
ability to change

Counterconditioning Substituting alternatives for anxiety-related behaviors

 

Stimulus control

Avoiding or countering stimuli that elicit problem
behaviors

 

Contingency management

Rewarding one’s self or being rewarded by others for
making changes

 

Helping relationships

Being open and trusting about problems with someone
who cares

 

Interpersonal control

Avoiding people or social situations that encourage
problem behavior; seeking people or situation that
encourage healthier behavior; restructuring social
relationships

 

 

Medication

 

Use of prescribed or nonprescribed substances directed
at appetite, metabolism or emotion

 

22

 

Most people do not maintain their desired behavioral change on the ﬁrst attempt.
Successful self-changers averaged three to four action attempts before attaining
maintenance to smoking cessation (Shachter, 1982). These ﬁndings led to the proposed
spiral pattern of change for behaviors.

The spiral model suggests that most relapsers do not revolve endlessly in circles, nor do
they regress all the way back to where they began. Instead, each time relapsers recycle
through the stages, they potentially learn from their mistakes and try something different
the next time around (DiClemente et al., 1991).

Several researchers have reported that it is possible for people to change behaviors
without expert assistance (Cohen et al., 1989; Orford, 1985). The behavior of such self-
changers, based on the Stages of Change Theory, are well documented (Prochaska et al.,
1995). Researchers have found the amount of progress clients make following
intervention tends to be a function of their pretreatment stage of change (Prochaska and
DiClemente, 1992).

To measure the Stage of Change, there are two ways of assigning stage: an
algorithm or a continuous measure. An algorithm is a short measure or series of
questions to categorize a subject into a single, discrete stage based on stage deﬁnition.
Several items (4-6) are used to assign every person to a stage using the algorithm.
Nutritionists often use this method rather than the continuous measure because it is
simple and relatively easy to assign clients into stages. A continuous measure, by
contrast, gathers information on each stage of change for an individual using several
questions for each stage with a Likert response format. Individuals are then classiﬁed

into groups based on their stage of change proﬁles (Reed et al., 1997). The measure

23

usually has eight items for each stage, Precontemplation, Contemplation, Action and
Maintenance. By this method, every perSon has a score for each stage and items can be
clustered into stages based on those scores. This method was the original tool used for
the Stages of Change Theory developed by psychologists. From it derives the
algorithmic method. Glanz et al. 1994 adapted this algorithmic method for dietary
behavior for fat and ﬁber intake (Table 6). Questions in the algorithm include self-rated
fat and ﬁber intake, time period for those intakes, behavioral intention to change diet and
reported eating habits changes such as attempts and success (Glanz et al., 1994).

Process of Change-2ml dimension“

Processes of change, the second dimension of the Stages of Change Theory (Table
4) provide important guides for intervention programs. Processes are the covert and overt
activities that people use to progress through the stages. The definitions of processes
have been explained in Table 5 (Bowen et al., 1994; Prochaska et al., 1992b).

The processes are selected by examining recommended change techniques across
different psychologic theories, which explains, in part, the term ‘Transtheoretical’
(Prochaska, 1979; Prochaska et al., 1992a). Table 4 shows what processes have been
applied at each stage by successﬁil changers. For example, psycho-analytic techniques,
attributed to Freud, are used to bring the unconsciousness or subconscious to awareness
or consciousness. These processes are useful strategies for those in the precontemplation
and contemplation stages. Therefore, consciousness raising, dramatic relief and
environmental reevaluation are applied for moving from preContemplation to
contemplation stages. Some techniques such as reducing perceived barriers and

increasing perceived beneﬁts derive from elements of the Health Belief Model. Other

24

Table 6. Stages of dietary change: Algorithm and items (Glanz et al., 1994)

 

 

Stagea Deﬁnition Items used
Maintenance Healthy dietb for >6 months Self-rated diet
Action Healthy diet for <6 months or tried to Self-rated diet
change with some success success in Reported changes: attempts,
the last 6 months Success
Preparation Tried to make healthy diet changes Self-rated diet
in last 6 months but not successful or Reported changes: attempts,
deﬁnitely plan to change Success
Behavioral intention to
change diet
Contemplation Maybe/probably plan to change Self-rated diet
Maybe/probably plan to change diet Reported changes: attempts
in the next 6 months; and no attempts success
to change in the last 6 months Behavioral intentions to
change diet
Precontemplation No plans to change diet in the next 6 Self-rated diet

months; and no attempts to change in
the last 6 months

Reported changes: attempts,
success

Behavioral intentions to
change diet

'Assignment to stages was done sequentially, beginning with maintenance. Once an individual was

assigned to a stage, the remaining response codes were not processed.

”Healthy diet=Low/very low fat, or high/very high ﬁber

25

 

 

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techniques such as expectation, expectancies and reinforcement from the Social Learning
Theory can be used for preparation and action stages. Social support techniques like
helping relationships are used as processes in the maintenance stage. Self-reevaluation is
used to progress ﬁ'om the contemplation to preparation stage. Self-liberation is used for
the movement from preparation to action. Contingency management,
Counterconditioning and Stimulus control, all from the Behavior Modiﬁcation Theory,
are emphasized in Action and Maintenance stages (Prochaska et al., 1997).
Consciousness raising, Dramatic relief, Environmental reevaluation, Social liberation and
Self-reevaluation are considered “Experiential processes” and Helping relationships,
Stimulus control, Counter conditioning, Reinforcement management and Self-liberation
are considered “Behavioral processes” (Prochaska et al., 1991).

The processes identiﬁed to date have been for behaviors other than eating ﬁ'uits
and vegetables, 10 processes for smoking cessation (Prochaska et al., 1988) and 12
processes for weight control (Table 5) (Prochaska et al., 1992b). However, use of
processes of change has been reported differently in some cases. Use of process in
pregnancy smoking cessation differed from the processes used in nonpregnancy smoking
cessation (Stotts et al., 1996). In that study, the behavioral process use for pregnant
women in the Action stage was similar to that of nonpregnant women in the Preparation
stage of change.

There are few studies on the processes of change for dietary practices. Eight
processes instead of 10 processes were found for eating a low-fat diet (Bowen et al.,
1994). A study reported a signiﬁcant difference in the use of 10 processes for low-fat

eating between people in precontemplation and those in maintenance (Cunpuu et al.,

26

 

 

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2000). No study has been published to date on the processes of change for fruit and
vegetable intake. It is important to identify the processes of change matched to each
stage to develop intervention techniques to increase fruit and vegetable intake.

To identify processes of change for eating behaviors, two factors must be
considered. When processes of change for smoking cessation were identiﬁed,
researchers found out that including relapsers in the analysis created an inconsistent
pattern of processes. When the relapsers were removed for the analysis of processes, a
clear pattern of processes appeared across the stages (Prochaska et al., 1984b). Another
important factor identifying patterns of process use is to consider those people in habitual
maintenance who practice the desired behavior unintentionally. In smoking studies, such
peOple, e.g. those who never smoked, were not included in the analysis.

Decisional balance-Part of 3’“l dimension

Decisional balance reﬂects the individual’s relative weighing of the pros and cons
for changing the target behavior. Therefore, it helps to understand the decision-making
process. Originally, Janis and Mann’s model of decision-making, which include four
categories of pros and four categories of cons, was used (Janis and Mann, 1977). Four
categories of pros were ‘gains for self’ and ‘to others’ and ‘approval for self” and ‘to
others’. F our categories of cons were ‘costs to self ’and ‘to others’ and ‘disapproval from
self” and ‘from others’. Many studies with these eight factors have been conducted, but
only two structures, pros and cons, were found in smoking cessation (Velicer et al.,
1985)

Analysis of 12 problematic behaviors assessed on the basis of the original concept

of decisional balance with 24 items demonstrated that progress from precontemplation to

27

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action involved an increase of approximately one standard deviation (SD) in the score for
pros of changing and 0.5 SD decrease in the score for cons of changing (Prochaska et al.,
1994). In seven of 12 behaviors such as smoking, quitting cocaine, condom use, weight
control, radon testing, safe sex and follow—up appointment with doctor, the crossover
point between pros and cons of the behavior occurred during the contemplation stage.
The crossover point for exercise was during the preparation stage (Prochaska et al.,
1994). For’sunscreen use, high-fat diets and mammography screening, the crossover
point was during the action stage. A decisional balance study with stage of change for
weight loss also showed that people could not differentiate eight constructs in pros and
cons, but investigators recommended using eight constructs in items to include all
possible considerations. However, it is not known if all eight constructs are equally
salient forall possible behavioral decisions (O’Connell and Velicer, 1988).

Other multidimensional approaches to decisional balance tested the external
validity of the Stages of Change Theory by comparing the pros and cons between stages
of change for drinking alcohol using different constructs in four categories such as
ability, emotion, interpersonal and practical (Migneault et al., 1997). Myers et al.
reported that exercise in young adults using four multidimensional beneﬁt factors (social,
psychological, body image and health) and four barrier factors (time-effort, social,
physical effects, and speciﬁc obstacles) explained stage of exercise adoption better than
the model with a smaller number of factors (Myers and Roth, 1997).

To decide which constructs we will use, a study with factors affecting the food
choices of young adults should be considered. One study using focus group interviews

with 57 young adults from 10 states (Stewart et al., 1994) identiﬁed several factors. The

28

factors affecting food choice included: 1) convenience; 2) calorie content; 3) health; 4)
price; 5) satiety (whether the food was ﬁlling); 6) friends; 7) advertising; 8) taste; 9)
habit; 10) appearance; 11) eating out; 12) cooking skills; 13) avoiding monotony; 14)
culture; and 15) cooking and storage facilities. Based on ﬁnding from these studies
reviewed here, a decisional balance instrument used in this dissertation was developed by
a 10-state regional research team (Betts et al., 2000).
Self-cfﬁcacy-Part of 3"1 dimension

Self-efﬁcacy is the situation-speciﬁc conﬁdence people have that they can perform
particular healthy behaviors in high risk situations without relapsing to their unhealthy
behaviors. This concept came originally from Bandura’s Self-Efﬁcacy Theory for
behavior change (Bandura, 1977). The self-efﬁcacy construct has been used as an
intermediate outcome of behavioral change to assess construct validity in studies with the
Transtheoratical Model. Self-efﬁcacy has been found to be low in the precontemplation
and contemplation stages, but higher in action stages (De Vries and Backbier, 1995).
There have been some arguments about whether self-efﬁcacy is a unidimensional or a
multidimensional construct. However, in general, the number of efﬁcacy dimensions is
reported to be determined by the nature of the problem area with situational determinants
(V elicer et al., 1990). In smoking cessation, three dimensions, positive/social,
negative/affective and habit/addictive, were found (V elicer et al., 1990). Negative
emotions, availability, social pressure, physical discomfort and positive activities were
found to be the ﬁve primary factors of efﬁcacy for weight control (Clark et al., 1991). In
a study to reduce dietary fat intake, a signiﬁcant difference in self-efﬁcacy for three

constructs -- negative affective, positive social and difﬁcult situations -- was found

29

among stages (Ounpuu et al., 1999). The self—efﬁcacy instrument used in this study was
developed by the 10-state regional research team (Betts et al., 2000).
Validity of Stage of Change assessment tool

Because this research is one primarily establishing the validity and reliability of
psychometric instruments, these concepts are discussed next. In this section types of
validity and reliability deﬁned and related to issues in this study.
Types of validity

Common methods used to establish validity of an instrument include content
validity, criterion validity and construct validity (Carmines and Zeller, 1974; Baranowski
and Simons-Morton, 1991). Content validity explains how closely the measure relates to
the state of knowledge concerning a speciﬁc area. For example, if an instrument is to
measure the risk factors of heart disease, then questions should include all the risk factors
for heart disease including smoking, alcohol, diet, exercise, family history, etc. Criterion
validity compares the results of a measure or instrument to a criterion measure or ‘gold
standard’. For example, if subjects were asked how many fruits were consumed, the
validity of their responses could be established by comparing the self-reported number of
servings to the number from an objective, surreptitious observation for fruit intake to
check whether those subjects in action and maintenance stages were eating more than 5
servings of ﬁ'uits and vegetables a day. Construct validity is used with quantitative
analysis, but lacks an identiﬁable and/or accepted criterion or standard. For this type of
validation, we examine whether the results of the measure used agree with what others
think measures the same thing (Patrick and Beery, 1991). For example, if placing people

into stages is done correctly, then other associated psychosocial factors should be

30

 

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appropriate for the stage in which people are, or a factor analysis will result in clusters
appropriate to the hypothesized theory.
Criterion Validity issues for Stage of Change assessment tool and dietary behavior
For healthy people of normal weight, most health problems related to diet do not
produce immediate or dramatic symptoms, and are not targets of social stigma as are drug
abusive behaviors. Eating habits usually do not produce short-term physical reactions,
involve guilt and seldom invoke social pressure to change. The exception here is
‘obesity’ for which over-eating can be one cause. Dietary behavior change for disease
prevention does not require the cessation of the behavior, but modiﬁcation. Not eating
food is neither possible nor desirable, unlike drug abusive behaviors (Glanz et al., 1994).
Some researchers argue that stage status in the Stages of Change Theory is
cognitive and self-perceived rather than overtly behavioral (Glanz et al., 1994). In this
case, the validity of a staging algorithm depends largely on people’s ability to accurately
self-rate their behaviors, perceive their own intentions and perceive the timeframe of
intentions to change a health behavior (Glanz et al., 1994). However, people’s ability to
self-rate their smoking or other drug abusive behavior likely differs from that of self-
rated eating behavior. Identifying one’s self-rated smoking or drug abuse behaviors is
fairly easy, because people are clearly aware of their abstinence from a substance, but
many might not be likewise aware of the quality of their diet. The ability to accurately
self-rate one’s own diet requires some awareness of the nutrient content of a current diet,
depends upon knowledge of the nutritional composition of foods, and an ability to

estimate portion size, as well as the concepts of a “healthy diet” (Glanz et al., 1994).

31

 

Therefore, criterion validity should be addressed for assessing eating behaviors, unlike
other behaviors such as smoking or condom use.

So far research with the Stages of Change Theory for food habits has been
conducted primarily using cross-sectional research designs to specify people’s stage of
change for fat and the associated psychosocial factors. The validity of the Stages of
Change instrument for fat has been reported as “fair”, because most research in the
Stages of Change Theory with dietary intake has shown a successﬁrl linear trend along
the stages. The degree of validity is questionable for actual fat intake, because linear
trend analysis fails to show certain criterion intake differences for people between pre-
and post-action stages (Glanz et al., 1994; Sporny and Contento, 1995; Brug et al., 1997).
One pilot study showed the correlation coefﬁcient between self-ratings of high—fat diets
and independently measured fat intake was r=0.49 (Glanz et al., 1994). In that study,
although dietary fat consumption decreased as the stage of change progressed and seemed
to support the validity of staging algorithms, people in the action stage still ate 37%
energy from fat and people in the maintenance stage ate 31.7% energy from fat. Another
study showed those in the action stage consumed 34% energy from fat and 31.6% in the
maintenance stage (Sporny and Contento, 1995). These ﬁndings support that people
were trying to change behavior, but the average person in the action stage had still not
reached the goal of 30% energy from fat. In a stage-matched intervention for reducing
fat and increasing ﬁuits and vegetables, fat intake was reduced to 35% of energy after
tailored intervention based on stages for fat intake. At the baseline, however, people who
ate 40% of energy ﬁ'om fat were considered as-in the action stage when intervention was

given (Campbell et al., 1994).

32

If signiﬁcant improvement in dietary behaviors is desirable, then a small change is
still something worth doing (Prochaska et al., 1995) and making these step-by— step
changes towards a goal is an important part of the change process. H0wever, the ultimate
goal according to the Stages of Change Theory is ﬁrll ﬁ'eedom from the problem. Cutting
in half the number of cigarettes over six months does not move a person to the
maintenance stage of smoking. Likewise, it is questionable that making some behavioral
improvement in eating should be deﬁned as action and maintenance’stages if people are
still eating more than 30% energy from fat even if they are eating better than before.
Other efforts to establish criterion validity in dietary behavior

To get behaviorally accurate Stages of Change, Green and colleagues tried another
approach using the “avoid” algorithm combined with behavioral markers including 5
items for low-fat intake to assign people to each stage (Greene et al., 1994). In the study
by Greene and colleagues, the people who were in action and maintenance stages showed
an average 29.9% and 28.5% energy from fat calculated from the 46-item food
frequency, respectively. Only 7% of the nonsmoking adults who consumed more than
30% energy ﬁom fat were assigned to post-action stages. However, 44% of the people
who consumed less than 30% of energy from fat were assigned to pre-action stages. To
overcome that kind of criteria problem with Stages of Change and increase the sensitivity
and speciﬁcity of the tool, the same researchers developed a Stage of Change algorithm
combined with the behavioral criterion which should be met -- less than 30% energy from
fat for post-action stages (Greene et al., 1998). Ifa person reported eating less than 30%
energy from fat at the baseline or for 6 months, but the person actually ate more than 30%

energy ﬁom fat, she or he was assigned to an unclassiﬁed stage. Without using a

33

behavioral marker, about 55% of the subjects fell into the unclassiﬁed group. Those
people in unclassiﬁed group were more likely than those classiﬁed to decrease fat intake
and move to an advanced stage alter getting feedback.

The next several studies discuss criterion validity for ﬁ'uits and vegetables. Stage
of Change based on self-reported fruit and vegetable intake from one study was validated
with the score from a 24-hour recall Food Behavior Checklist which asked the frequency
of intake 16 foods, four related to ﬁuits and vegetables (Laforge et al., 1994). Stage of
Change based on self-reported fruit and vegetable intake showed a linear relationship
with the hit and vegetable intake score and Stage of Change. However, the Stage of
Change was not compared to the actual number of servings of ﬁ'uits and vegetables
consumed by subjects. In one EFNEP study, the correlation coefficient between fruit
intake from a 2 day dietary record and self-reported frequency of mm intake used in a
Stages of Change questionnaire was F045, p<0.01. For vegetables it was r=0.21,
p<0.05 (Chung and Hoerr, 1998). Those correlation coefﬁcients were signiﬁcant with a
slightly better estimation for fruits than for vegetables. Estimation by clients themselves
can not generally be considered accurate. Only 58% of people who consumed less than 5
servings of ﬁ'uits and vegetables perceived they ate less than 5 servings of fruits and
vegetables, whereas, 82% of people who consumed more than 5 servings of fruits and
vegetables reported they ate more than 5 servings of fruits and vegetables.

Therefore, to determine the Stage of Change for fruit and vegetable intakes in this
study, the recommended number of servings of fruits and vegetables will be used as the a
standard criterion to test criterion validity. Separation of adequate from inadequate fruit

and vegetable intake is needed to place people to each stage for eating fruits and

34

vegetables. A recent study shows substantial numbers of subjects who were classiﬁed in
action and maintenance stages by self-reported questions actually had fruit or vegetable
intakes below the recommended levels (Brug et al., 1997). Brug and colleagues argued
that such subjects might be better classiﬁed as precontemplators if they are unaware of
their need to change (Brug et al., 1997). Another study of the Stages of Change Theory
with ﬁuit and vegetable intake also reported similar ﬁndings (Lechner et al., 1997).
Investigators used the staging algorithm for self-rated ﬁ'uit and vegetable intake
comparing fruit and vegetable intake to an eight-item food ﬁequency. They reported that
“ the lack of congruence between estimated objective and self-rated behavior appears to
seriously lower the internal validity of the stage algorithm.” These researchers concluded
that a stage algorithm combining objective and subjective consumption with subjects’
- intention for eating would have better validity (Lechner et al., 1997).
Studies of construct validity

Most validation studies for the Stage of Change Theory have used construct
validity using continuous measures of Stage of Change by McConnaughy et al. rather
than categorized as earlier described (McConnaughy et al., 1983; Domel et al., 1996;
Cardinal, 1997; Willoughby et al., 1996). Construct validation is likely the best test when
a standard criterion is not accepted by researchers. Willoughby et al. ’8 study with alcohol
use showed four components and some construct validity with anxiety and depression
(W illoughby and Edens, 1996). This research group used the continuous Stages of
Change instrument and found only two cluster stages -- precontemplation and
contemplation/action stages. Participants in the precontemplation cluster reported being

less worried about their alcohol use and less receptive to help. By way of contrast,

35

Cardinal used a categorical Stages of Change tool to test the construct validity by
comparing differences between the stages in Body Mass Index (Kg/r112), ﬁtness, exercise
behavior, barriers and self-efficacy. Signiﬁcant between-stage differences were found for
these variables (Cardinal, 1997), which he concluded demonstrated discrete Stages of
Change.

There have been studies testing the construct validity of the Stages of Change
Theory for fruit and vegetable intakes. Domel and colleagues used 32 items for a
continuous Stage of Change questionnaire to get principal components and clusters based
on the desired component (Domel et al., 1996). Against these identiﬁed components of
stages, they then tested the construct validity by comparing actual fruit and vegetable
intake, self-efﬁcacy and outcome expectation for fruit and vegetable intake between the
stages identiﬁed in the factor analysis. For children (age 8-9 years old) only two
components -- precontemplation and beyond precontemplation -- could be identiﬁed.
This two-stage (as Opposed to 5-stage model) might have resulted from an inability to
understand the questions by the children due to an immature level of cognitive
development. However, self—efﬁcacy and outcome expectations, but not actual fruit and
vegetable intake, did increase with the advanced stages. Another study comparing stages
of change to attitude, self-efficacy and actual ﬁuit and vegetable intake showed similar
results with Dutch adults (Brug et al., 1997). In that study, fruit and vegetable intakes
were not signiﬁcantly different among stages, but self-efficacy was.

Reliability of Stages of Change assessment tool
For reliability analysis, there are three types of tests used: split-half reliability;

test-retest reliability; and the calculation of Cronbach ’s alpha (Patrick and Beery, 1991).

36

Split-reliability tests are conducted by dividing in half the total number of items in a
instrument and comparing the results obtained from each half using a correlation
coefﬁcient. T est-retest is when the same individuals are measured at two points in time
and the results are compared by correlation. In this case, responses to a repetition of the
testing tool mightbe affected by taking the initial test itself. This can result in so-called
“learning effect”/and change results on the second test. Cronbach ’s alpha is used to test
the internal consistency of items looking at how different items or questions ﬁt together.
It is also used to test consistency in a construct measured by several items.

Test-retest reliability within one week has been reported a study of the Stages of
Change Theory for exercise using a categorical Stage of Change tool. Spearman’s rho
for the stage of exercise measure was very high, 0.96 (Cardial, 1997). In another study,
test-retest reliability for fruit and vegetable intakes were F054 for the
“precontemplation” stage and r=0.70 for the “beyond precontemplation” stage over a 2-
week period using a continuous Stages of Change tool with fourth- and ﬁfth-grade school
children (Domel etal., 1996). In the same study, Cronbach’s alpha testing also was
conducted to examine the internal consistency of questions within each stage construct,
because continuous measures were used to assign stages in this study.

Another study on Stages of Change Theory using a longitudinal approach
examined the stability of stages over time. More than half of the subjeCts in the
precontemplation and contemplation stages for fat intake at baseline failed to progress at
all over an 18-month period when given behavioral feedback. Subjects in the preparation.
stage were the most dynamic demonstrating in both forward and backward stage

movement (Greene and Rossi, 1998). About a third of the subjects in the preparation

37

stage at 12 months regressed, primarily to contemplation at 18 months, and 29%
progressed to action, when they were given feedback at 12 months. However, in the
study by Greene and his colleagues, it was not clear whether some people in the
preparation stage actually relapsed into the contemplation stage at 18 months or whether
they were actually in the contemplation stage at 12 months, instead of in the preparation
stage, because the survey period intervals were 6 months. In regards to the stability of
stages, one could argue that when people in the preparation stage do not move toward the
action stage within one month, then the Transtheoretical Model fails this deﬁnition for
preparation stage (Pierce et al., 1996).
Direction for developing Stage of Change assessment tool for dietary behavior

A problem behavior can be considered solved once you attain the criteria that
health professionals agree places you at zero or minimal risk for a particular behavior
(Prochaska et al., 1995). The purpose of behavior change by Stage of Change is that
people take action to solve the problem, not just improve. it (Prochaska et al., 1995). To
achieve this purpose, action criteria for problem behaviors must be set for each behavior.
However, it is difﬁcult to set criteria or to get accurate measures by self-assessment for
some non-discrete behaviors like physical activity or eating. For ﬁuit and vegetable
intake, the consumption of the recommended number of servings of ﬁuit and vegetable
intake has been used as the action criteria.~ Therefore, eating at least two servings of
ﬁuits and three servings of vegetables can be used as the criterion to place people into
post-action stages because those are the current goals for ﬁuit and vegetable intake in the
U.S. However, if people frequently estimate their ﬁ'uit and vegetable intake inaccurately,

by either over or under estimation or both, then people will not be assigned to their actual

38

 

Stage of Change. The effort of the self-changer or interventions by professionals using
the Stage of Change Theory would then be ineffective and inefﬁcient.

To adapt the Stage of Change Theory for eating fruits and eating vegetables from
the Stage of Change Theory for problematic behaviors to assign people into appropriate
stages, we need to keep in mind another factor as well. Most studies on the Stage of
Change Theory with problematic behaviors have focused on only people who have
problem behaviors to change because investigators assumed that people are able to
recognize their problematic behaviors and used self-rated data. However, this
dissertation study will test whether people can recognize their dietary behavior using
adequate and inadequate fruit and vegetable intake.

In this dissertation study, the Stages of Change tool based on the algorithm, the
method preferred by nutritionists, with short items will be used to assign people to each
stage. Scientiﬁc observation will also compare stages by actual ﬁuit and vegetable intake
from a 3 day record to stages with three kinds of assessment method for fruit and
vegetable intake -- self-rated, 24-hour recall and food frequency -- for criterion validity.
Results of using these staging instruments with also be compared to subjects’ process and
decisional balance scores and self-efﬁcacy to assess construct validity.

Few reliability studies have been reported with Stages of Change because of the
natural dynamics of the Stages of Change Theory. People can change their behavior
without assistance within a certain time period. However, the reliability for Stages of
Change based on three assessment methods willube tested in this dissertation research
because measuring reliability for an inconsistent behavior like eating may be worthwhile,

although it confounds reliability results with learning and change.

39

Chapter Three

Validity of Stages of Change Instruments For Eating Fruits And Vegetables

A. ABSTRACT

Objective To establish outcome validity for “stage of change instruments” to assess
eating the recommended number of servings of fruits and vegetables.

Participants A convenience sample of 294 college students were recruited from
introductory nutrition classes for this study.

Design The servings of fruits and vegetables, separately, from three types of staging
methodsnself-rated intake, 24-hour recall and food frequency (F F Q)-were compared to
the servings from a three-day food record. The outcome validity was assessed based on
whether or not at least two servings of fruits and three servings of vegetables were
reported.

Analysis Validity was assessed by sensitivity, to measure the ability to detect low
intakes, and by speciﬁcity, to measure ability to detect adequate intakes. Cohen’s Kappa
was used as well to examine the agreement between the three staging methods and a
three-day food record.

Results For fruits, sensitivity was best using a 24-hour recall (K= 0.81). The recall also 1
showed the best agreement with a three-day food record for servings consumed by people
in pre-action or post-action stages. For vegetables, however, all three methods had low
agreement with the results of a three-day food record. Self-rated intakes for vegetables
had the best sensitivity (K = 0.66) and FFQ had the best speciﬁcity (K=—0.73).
Application/conclusions Dietitians can use the 24-hour recall methods to identify

people who consume inadequate servings of fruit. To detect adequate vegetable intake,

40

the FFQ was best of the three methods. Dietitians should probe for vegetables in mixed

dishes and on sandwiches.

B. INTRODUCTION

The Transtheoretical Model (TTM) explains the pattern of people’s behavioral
change by integrating concepts and techniques from different behavioral theories
(Prochaska, 1979; Prochaska et al., 1992a; Glanz et al., 1994), and the TTM has been
tested with several problem behaviors (Prochaska et al., 1994). The TTM has a central
organizing construct, the Stages of Change, for which the assumption is made that people
can recognize their own intentions to change a speciﬁc health behavior. Recognition of
intention to change behavior is a necessary step to assign people to pre-action stages:
precontemplation (no intention to change); contemplation (thinking about change); and
preparation (making plans to change behavior in the near ﬁrture or have made some
changes but not reached a particular criterion). Likewise, people must be able to
recognize the time period within which they are making current health changes in order
for health practitioners to determine people who are in post-action stages. Post-action
stages include action, actively changing behavior, and maintenance, maintaining desired
behavior.

The TTM has been applied to changing dietary behaviors such as reducing fat
intake and increasing ﬁuits and vegetables (Curry et al., 1992; Greene et al., 1994;
Campbell et al., 1998; Brug et al., 1997), because the theory is relevant to all health
behaviors (Prochaska et al., 1997b). What differs most noticeably from drUg cessation in
the use of the TTM with diet is the estimation of the target behavior. Unlike the change

for drug abuse behaviors, dietary behavior change for disease prevention requires the

41

modiﬁcation, not cessation, of the behavior. Dietary behavior is not an addictive
behavior which must be avoided for health. Not eating is neither possible nor desirable,
whereas cessation of drug abusive behaviors is the target (Glanz et al., 1994). Target
dietary behaviors using the TTM are eating recommended amounts and types of speciﬁc
foods. The fact that people do not know dietary recommendations is a problem for
dietitians. Previous studies on the TTM, which relied only on people’s “perceptions of
their behavior,” showed a linear relationship for fat intake and for ﬁuit and vegetable
intakes across the stages from precontemplation to maintenance (Greene et al., 1994;
Spomy and-Contento, 1995; Laforge et al., 1994; Hoerr et al., 1997). Stage assessment
based on self-reported dietary intake has failed to show validity in terms of a behavioral
criterion of achieving the dietary goal, likely because people were unaware of whether
they were eating the recommended amount of food or nutrient (Brug et al., 1997; Glanz
et al., 1994; Spomy and Contento, 1995).

To use the TTM to change dietary behavior, dietitians need other objective
methods in addition to people’s own generalized perceptions of adequate or inadequate
intakes (in this study called “self-rated intake”). Such objective methods are necessary to
establish the validity of assessment and evaluation, especially criterion validity, which
compares the behavioral outcome of stages to a criterion measure or ‘gold standard’
(Cheney, 2000). Measurement of reliability based on the outcome of staging instruments
for a behavior like eating, which typically varies from day to day, is important. The
objectives of this study were to establish outcome validity and reliability for types of
methods to classify the stage of change for eating adequate amounts of ﬁuits or

vegetables, separately.

42

C. METHODS
Respondents and Procedure

’A convenience sample of college students aged 18-24 years was recruited during
the winter from two introductory nutrition classes at a large, north central, landgrant
university. The response rate from the two classes was 51% for a baseline sample of 360
subjects. Extra points toward class grades were given as an incentive to complete the
baseline questionnaire including a three-day record. Subjects with incomplete dietary
data (n=66) were excluded, including 44 subjects with incomplete sets of dietary records.
Data were usable from 294 subjects. Eighty percent were female; 86% were white; 63%
lived in campus residence halls. I

From this sample of 294, 123 subjects participated in the test-retest of the three

stage classiﬁcation methods. A coupon to a campus snack shop was given for
completion of the retest. The average time between test and retest was 11 days. Separate
consent forms were signed for data collected at the baseline and for the retest.
Questionnaires .

A set of questionnaires about, ﬁuits and vegetables was distributed at the baseline.
~ The questionnaires included three different methods to classify the stages of change and a
three-day food record. The three types of assessments for comparing outcomes with the
three-day food record for stages of change were: a) self-rated intake; b) a 24-hour recall;
and c) a food frequency for ﬁuits and vegetables.

Stages for fruit intake and for vegetable intake were measured and classified
separately by the three different outcome assessments all using the same concepts of

intention and time period of current intake (Glanz et al., 1994; Hoerr et al., 1997)

43

(Figures 1 and 2). For evaluating achievement of the outcome criteria by all three
methods, the cutoff points were two servings of fruits and three servings of vegetables.
Fruits included fruit juice. Vegetables included fried potatoes, vegetable juice and ’
vegetables in mixed dishes.

Self-Rated Intake. The ﬁrst method for classifying the stages of change
(Figure 1) used the question for self-rated intake, “How many servings of
ﬁuits/vegetables do you eat a day?” The responses were marked 0-4+ for ﬁuits and 0-5+
for vegetables with 4+ and 5+ truncated to 4 and 5, respectively, in the calculations.
Subjects were classiﬁed into categories for action or maintenance stages, if self-rated
intake met the outcome criteria. A further division between action and maintenance was
determined with a question about the time period. Subjects who did not meet outcome
criteria were assigned to one of the pre-action stages of precontemplation, contemplation
or preparation. Respondents were classiﬁed as in the precontemplation stage when they
had no intention of eating two or more servings of fruits or three or more servings of
vegetables. Subjects were placed into the contemplation stage when they intended to eat
these amounts within six months. They were considered to be in the preparation stage
when they intended to eat the recommended servings of fruits and vegetables within 30
days.

24-hour food recalls. A 24-hour recall was self-reported as an outcome assessment
for the second staging method at baseline and later for reliability. Subjects were
instructed to recall foods according to the USDA multiple pass method (Moshfegh et al.,
1999). Subjects were classiﬁed into action or maintenance stages when they met

outcome criteria of the recommended number of servings'of fruits or of vegetables.

 

5.

 

Further classiﬁcation was made using the same concepts described for the self-rated
intake (Figure 2).

Food Frequency. For the third outcome assessment, separate food frequency
questionnaires (FFQ) for fruits and for vegetables over the past week included 12 fruit
items and 14 vegetable items (Figure 2). These short FFQs provided three options for
serving sizes (small, medium and large) and a seven-level scale for frequency of intake
from less than one per week to two times a day or more. Two times a day was considered
to be two servings per day. This FFQ, adapted from the National Cancer Institute’s
Health Habits and History Questionnaire (Thompson et al., 1994), was developed by a
10-state research project team for young adults (Betts et al., 2000).

Three-day food records. Three-day food records on two consecutive weekdays
and one weekend day served as the “gold standard” and were collected at the baseline.
Average fruit and vegetable servings were calculated from three days of food records to
compare the servings of ﬁuits and vegetables to the three staging methods. Subjects were
instructed to report all food they ate, and detailed instructions were provided to increase
the accuracy of recalls and records.

Calculation of fruit and vggetable servings. For 24-hour recalls and a three-
day record, the food servings database for the 1994-96 USDA Continuing Survey of Food
Intakes by Individuals (CSFII) was used to count fruit and vegetable servings (U.S.
Department of Agriculture, 1998). The Expanded Food and Nutrition Education Program
(EFNEP) Evaluation/Reporting System (U .S. Department of Agriculture, 1994) was used
as the nutrition software to calculate servings of fruits and vegetables (Database=1540

food items). The EFNEP Evaluation/Reporting System (ERS) was selected because it

45

can be used to calculate both food servings and nutrients, it has an accessible database for
corrections, and data can be exported for further statistical analysis. Because the ERS
was designed prior to release of the CSFH Food Guide Pyramid servings database, some
discrepancies were found between the servings in the CSFII and in the ERS. Therefore,
the database of the ERS was revised for such foods by counting fruits and vegetables on
the basis of the CSFII servings using the Microsoft Access program (version 7.0).
Analysis

The Statistical Package for Social Science (version 7.5 for Windows) was used for
data analysis. After running a normality test for the servings of ﬁuits and of vegetables, a
square root transformation was performed where needed. Differences in the average
servings of ﬁuits and of vegetables among the stages by all three methods were compared
using ANOVA. Cohen’s Kappa was used to calculate the agreement between the three
staging methods and averages of ﬁuit and vegetable intake from a three-day food record,
here considered as the “gold standard”. A Kappa _>_0.4O was considered good agreement
(Rosner, 1995). Outcome was examined by pre- versus post-action Stages of Change,
because we were interested in which staging method best predicted eating at least two
servings of him and 3 servings of vegetables - the amount achieved only in the post-
action stages of action and maintenance. Sensitivity, the ability to detect who had less
than two servings of fruits and less than three servings of vegetables, and speciﬁcity, the
ability to detect who had at least two servings of fruits and three servings of vegetables,
were calculated to examine which method measured intakes most accurately (Rosner,
1995). Because the ability to detect people with low intake is more important for

nutrition education than the ability to detect people with adequate intake, sensitivity was

46

the focus for validity testing. A program was written for Microsoft Excel to test the
signiﬁcant difference between Kappa values for the three staging methods (Donner et al.,
1996). Test-retest reliability of all three methods for stage classiﬁcation was also

compared using Kappa.

D. RESULTS
' Average intakes for fruits were 2.5: 1 .2 servings by self-rated intake, 21:22 by

24-hour recall, 3. 1 :23 by food ﬁequency and 2.0 :1.7 by a three-day food record.

 

Average intakes for vegetables were 23:1.2 servings by self-rated intake, 32:23 by
24-hour recall, 4.0:26 by FFQ and 3.1 :1.9 from a three-day food record.

Approximately 65% and 66% of the subjects were assigned to the post-action
stages of action and maintenance for eating at least two servings of ﬁuits by staging the
self-rated intake and the FFQ, respectively, whereas 42% were in post-action stages by
the 24-hour recall for ﬁuits (Table 1). For vegetables, 42% by the self-report, 49% by
recall and 57% by FFQ were in post-action stages. For ﬁ'uits, all staging methods
demonstrated on average <2 servings in pre-action stages and 32 in post-action stages.
For all three staging methods, the average ﬁuits intakes between pre-action and post-
action stages were signiﬁcantly different. Results were similar for vegetables for which
three servings per day was the behavioral criterion.

Figure 3 shows that the recall was best for staging fruits (Kappa=0.54) to detect
intakes, using the ﬁuit intakes from a three-day record as the behavioral criterion, when
compared to self-rated (K=0.31) and FF Q (K=0.29). Sensitivity, the ability to detect

people who ate less than two servings of fruits, was the highest for Recall (K=0.81)

47

compared to self-rated intake (K=0.49) and FFQ (K=0.47) (Figure 4). The ability to
detect peOple who ate at least the recommended number of ﬁ'uit servings, speciﬁcity, was
slightly higher using the self-rated intake (K= 0.85) and FFQ (K=0.84) than with the 24-
hour recall (K=0.73). For vegetables, however, agreement between stages and vegetable
servings from a three-day food record was low for all three methods: the self-rated intake
had a Kappa of 0. 17); the recall, 0.21; and the FFQ, 0.27. When sensitivity and
speciﬁcity for vegetables were calculated, the self-rated intake showed the highest
sensitivity (Kappa=0.66) and the FFQ showed highest speciﬁcity (Kappa=0.72).
Reliabilities or agreements between the stages at the baseline and the stages about
11 days later on average, were similar and acceptable for all‘three staging methods for
ﬁ'uits (Figure 5). For vegetables, the 24-hour recall had poor reliability compared to the

self-rated intake and the FFQ.

E. DISCUSSION

When Subjects were examined as a group, the average ﬁuit and vegetable servings
from all three outcome assessments could distinguished between pre- and post-action
stages. For individuals, however, different approaches might be needed for fruits and for
vegetables to most accurately assign people to behavioral stages. Stage classiﬁcation by
the 24-hour recall had the best validity and acceptable reliability. All stage classiﬁcations
failed to show good validity for individual vegetable intake using sensitivity to test
validity. Both self-rated intake and the FFQ with 12 fruit items showed good ability to
detect people with adequate ﬁ'uit intakes. However, vegetable intake showed a different

pattern. None of the three assessments demonstrated good agreement with vegetable

48

servings from a three-day food record, although FF Q was the best. Therefore, neither
people’s perceptions about their vegetable intake nor yesterday’s intake was good for
stage assessment using a behavioral outcome criterion.

All three methods, except the 24-hour recall for vegetables, showed good reliability
for both fruits and vegetables. Early research on TTM did not discuss the reliability of
staging instruments, because people can change their behavior without assistance within
a certain time period (Cohen et al., 1989; Orford, 1985 Prochaska et al., 1995). However,
it may be worthwhile to measure the reliability of Stages of Change assessment for
variable behavior like food intake, even though the results of the assessment of reliability
can be confounded somewhat by real change and by a learning effect ﬁ'om the study itself
(Mertens, 1998).

After several studies on adapting TTM to dietary behaviors using perceived intake
failed to show validity in terms of a behavioral criterion to achieve the target behavior
(Glanz et al., 1994; Spomy and Contento, 1995; Brug et al, 1997), other. staging methods
were tried for fat, ﬁuit and vegetable intakes. Those studies using behavioral criteria
such as a food checklist or FFQ to reassign people to stages have shown better validity
for staging than have the traditional methods of using people’s own perceptions (Greene
et al., 1994; Greene and Rossi, 1998, Laforge et al., 1994; Lechner et al., 1997; Lechner
et al., 1998; Van Duyn et al., 1998). Food ﬁ'equencies have shown good validity as a
staging method for dietary fat (Greene et al., 1994; Greene and Rossi, 1998). Other
investigators have shown, however, that FFQ tends to overestimate ﬁuit and vegetable
intakes compared to self-rated intake, as it did in this study (Serdula et al., 1993; Krebs-

Smith et al., 1995c; Calvert et al., 1997). Our FFQ method assigned more people to post-

49

action stages for both fruits and vegetables compared to other methods. Self-rated intake
also showed some overestimation of fruit intake, resulting in more people staged to post-
action stages. F or vegetables, however, the average number of servings of vegetables by
self-rated intake was underestimated compared to the intake by a three-day food record.
This underestimation by self-rated intake and overestimation by F FQ for vegetables
might lead to relatively high sensitivity for self-rated and relatively high speciﬁcity for
FFQ. This same pattern of overestimation for fruits and underestimation for vegetables
has been shown in a previous study with limited uncome women (Chung and Hoerr,
1998). In the present study, FFQ was positioned with self-rated intake on the same page
of the instrument. Thus, the servings from self-rated intake were possibly inﬂuenced by
the prior completion of the FFQ. Nevertheless, the FFQ still overestimated ﬁuit and
vegetable intakes compared to the simple, self-rated intake.

For eating vegetables, ﬁrrther studies on stage assessment are likely needed
because all classiﬁcation methods failed to show good agreement with vegetable servings
in our population Vegetable intake varied over time and estimation is more difﬁcult than
for fruit. Some of the difﬁculty with estimation of vegetable intake is likely due to a high
consumption of mixed dishes such as pizza, tacos, spaghetti, etc.

One study pointed out that re-classiﬁcation of stages using an objective estimation
for dietary intake poses a separate problem, because it combines people who believe they
are eating healthy and those who do not. The authors suggested that “it may be better to
treat maintainers who are not actually eating healthy as a separate group for separate
intervention’rather than re-classifying them” (Povey et al., 1999). However, to ﬁnd the

maintainers who are not actually eating healthy, we still need to estimate people’s dietary

50

intake. For a population study, multiple days. of food recalls or records for assessment
and evaluation would pose a high respondent burden resulting in loss of participants.
Therefore, we still need simple dietary assessment to determine which people eat healthy
and which ones do not.

When the readiness to increase intake, or to eat at least the recommended number
of fruits and vegetables was measured, the distribution of stages varied according to the
population (Hoerr et al., 1997; Campbell et al., 1999). Our data showed more people for
fruits and fewer people for vegetables in action and maintenance stages using self-rated
intake than a general adult Dutch population (Brug et al., 1997). This difference might
reﬂect cultural differences and characteristics of college students who think convenience
is the most important factor in food choice, because fruits are considered more
convenient than vegetables (Betts et al., 1995).

There are several limitations to this study. First, our subjects were a convenience
sample of mostly white college women in the north central U.S. and were people with
some interest in nutrition. Results cannot be generalized to all college students.
Baranowski and colleagues have reported that three weekdays and two weekend days
were needed to get a'consistency of 0.70 intraclass correlation for ﬁuit and vegetable
intakes (Baranowski et al., 1997). However, to reduce selection bias by a high
respondent burden, we used three days-two weekdays and one weekend day-as the gold
standard for agreement. Further studies are needed to replicate our results using another,
large population over different seasons.

. Most studies comparing dietary assessments have used correlation coefficients for

nutrients. However, our study used sensitivity and agreement for whether the

51

recommended servings of ﬁuits and of vegetables were reported by three methods.
Although most previous studies on TTM for eating ﬁuits and vegetables have not
examined them separately, our data showed a clear difference with regard to how people
perceived ﬁuits and vegetables. Therefore, ﬁrrther’ research should separate eating fruits
and eating vegetables, even though the S-A-Day message for the public has been
combined into one slogan. Longitudinal studies to follow people’s behavior and
psychosocial factors over time are needed to ﬁnd true relationships in changing food

behaviors.

F. APPLICATIONS

o Dietitians can quickly evaluate a person’s usual fruit intake by 24-hr’ recall when they
assess stages of readiness to eat ﬁuits.

- For vegetables, all methods distinguished between adequate and inadequate intakes
for groups, but none worked well for individuals. Dietitians must recognize that
people are not educated well enough to recognize vegetables or portion sizes
recommended for the Food Guide Pyramid and thus, they should probe for vegetables
in sandwiches and mixed dishes. ’

0 To distinguish simply between pre- and post-action stages for ﬁuits and vegetables

for groups of people, Dietitians can use any of the three staging methods.

52

 

 

How many servings of fruits and fruit juices (vegetables) do you usually eat
each day?

 

 

l

 

 

Fruit & fruit juice <2 serving
(Vegetable <3 serving)

 

 

l

 

 

Do you intend to start eating 2

 

 

 

Fruit & ﬁuit juice 22 serving
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next 6 month next 30 days 6 months 6 months
i v v I +
Precontemplation Contemplation Preparation Action Maintenance

 

 

 

 

 

 

 

 

 

 

Figure 1. Method A (Self-rated intake) of classifying the stages of change

53

 

 

 

Count fruit (vegetable) servings from a 24-hr. recall ---------- - Method B

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Count ﬁuit (vegetable) servings from a food frequency -------- Method C
i L

Fruit & fruit juice <2 serving Fruit & ﬁ'uit juice 22 serving

(Vegetable <3 serving) (Vegetable 23 serving)
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(vegetables) in the next 6 months? way?

i t i +
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next 6 month next 30 days 5 months 6 months

 

 

 

 

 

 

 

 

 

 

 

l v v v v

Precontemplation Contemplation Preparation Action Maintenance

 

 

 

 

 

 

 

 

 

 

 

 

Figure 2. Method B (24-hr. recall) and Method C (FFQ) for classifying the stages of
change . ‘

54

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Figure 3. Agreement for detecting intake from three staging methods compared to
3-day food record using behavioral criteria

56

 

 

   
 
 
 
   
 

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Sensitivity or

speciﬁcity 0,4 IMethod B
0.2
0.1

D Methcd C

 

 

 

 

Fruit Vegetable Fruit Vegetable
Sensitivity Speciﬁcity

Figure 4. Ability of three staging methods to detect low and adequate fruit and
vegetable intake, Sensitivity and Speciﬁcity, respectively (n=294)

57

 

 

 

 

 

 

 

 

 

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I Method B
D Method c

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Fruit

Figure 5. Test-Retest Reliability of Stage agreements by three staging methods (n=123)

58

Chapter Four
Developing an Instrument to Measure the Processes Young Adults Use for Eating

Fruits and Vegetables

A. ABSTRACT

The purpose of this study on him and vegetables was to identify processes of change
and to identify relationships between the processes and the stages of change. Items from
the 11 process constructs were generated ﬁ'om other health behaviOrs; then focus group
interviews were conducted to clarify wording and to ﬁnd additional items. Eighty items
were tested on a sample of 151 students in an introductory nutrition class. Using
conﬁrmatory factor analysis, 29 items resulted in 7 constructs: Health Concerns, Self
Reevaluation, Social Liberation, Health Commitment/Action, Interpersonal Control,
External Reinforcement and Helping Relationship. The coefﬁcient alpha was calculated
for each and the reliabilities, ranged from 0.69 to 0.85, i.e., acceptable for these 3 to 6-
item scales. The relationship between use of processes and stages were examined with
294 collegiate young adults. Signiﬁcant differences on self reevaluation and health
commitment for ﬁ'uit intake were found among stages. For vegetables, only health
commitment differed between groups (p<0.05). However, when the relapser and
maintenance groups were considered, health concern and self-reevaluation also differed
among stages (p<0.05). For both fruits and vegetables, “Health Commitment” and
“Self-reevaluation” were important behavioral strategies which differed between groups;

“Health Concerns” was an additional strategy for vegetables.

59

Key Words: Fruits; Vegetables; Behavioral Theory; Transtheoretical Model; Instrument

B. INTRODUCTION

Many studies have shown the beneﬁts of eating adequate fruits and vegetables to
reduce risk for chronic diseases (Steinmetz and Potter, 1996; Ness and Powles, 1997).
Because of the health beneﬁts, the national objectives in the U.S. have been set at ﬁve or
more servings of fruits and vegetables per day (USDA & USDHHS, 1992; USDA &
USDHHS, 1990; National Research Council, 1980; U.S. Department of Health and
Human Services, 2000). Nutrition educators remain frustrated in attempts to help people
meet this objective, because most Americans fall short of these recommendations (Krebs-
Smith et al., 1995a; Li et al., 2000). Several studies also revealed many young adults do
not eat the recommended number of fruits and of vegetables, even though dietary habits
of young adults relate to their current and later health (Song et al., 1996; Ma and Betts,
1999). Helping people achieve this national objective for fruits and vegetables has been a
challenge for nutrition educators (Cullen et al., 1998; Ciliska et al., 2000).

Stage of Change Theory has been used as a successful behavioral model to change
smoking and drug abuse behaviors. Such success has encouraged nutrition educators to
become interested in applying Stage of Change to dietary behaviors as well. The Stage of
Change Model can be depicted in three dimensions, which are: 1) the stages of change;
2) processes of change; and 3) decisional balance, self-efﬁcacy and temptation. The ﬁrst
dimension of the model, has ﬁve stages: precontemplation, contemplation, preparation,
action and maintenance assigned by the temporal, motivational and constancy aspect of

change (DiClemente and Prochaska, 198 5). The second dimension-processes of change-

60

-are the covert and overt activities that people use to progress through the stages. These
processes provide important guides for intervention programs and have been selected
from recommended techniques for change across various psychological and behavioral
theories, which explains another term for this theory, “Transtheoretical” (Prochaska,
1979; Prochaska and DiClemente, 1992).

Ten to 12 processes of change have been identiﬁed for smoking cessation and
weight control. These are consciousness raising, dramatic relief, self-reevaluation,
environmental reevaluation, social liberation, self-liberation, stimulus control, counter-
conditioning, contingency management and helping relationship in smoking cessation.
Weight control has two additional processes of change, which are interpersonal control
and medication (Prochaska et al., 1992b). Studies in smoking cessation have shown that
people in contemplation stage rely more on consciousness raising and people in action
emphasize other behavioral modiﬁcation processes to move into advanced stages, and,
ﬁnally change their behavior (Prochaska and DiClemente, 1983; Prochaska et al., 1991).
For example, the psycho-analytic techniques attributed to Freud, are used to bring the
unconsciousness or subconscious to awareness or consciousness. Such processes or
techniques appear to be useﬁrl strategies for those in precontemplation and contemplation
stages. Therefore, consciousness raising, dramatic relief and environmental reevaluation
are processes educators can use to help people move from precontemplation to
contemplation stages. Self-reevaluation is used to progress from contemplation to
preparation stages. Self-liberation is used for the movement ﬁom preparation to action.
Contingency management, counter-conditioning and stimulus control, all ﬁ'om Behavior

Modiﬁcation Theory, help those in action stage transition to the maintenance stage.

61

Social support techniques such as helping relationships are used as processes for those in
the maintenance stage (Prochaska et al., 1992a; Prochaska and Velicer, 1997a). Such
well-established interrelationships between stage of readiness to change and processes of
change have helped health educators develop effective intervention programs for
smoking cessation. However, before we apply this theory to dietary behavior, we must
know if the same processes exist for eating ﬁ'uits and eating vegetables as for smoking or
drug abusive behavior. It is likely that each health behavior has some unique aspects.

Few studies have reported the applicability of the processes of change to dietary
behavior. A study conducted on low fat dietary change found eight processes--
environmental reevaluation, self reevaluation, dramatic relief, social support,
consciousness raising, behavioral strategies and social liberation (Bowen et al. , 1994).
The relationship between the stages and a process like social support has been studied for
eating ﬁuits and vegetables (Sorensen et al., 1998). However, no studies that identify all
the processes of eating fruits and eating vegetables for young adults have been published
to date. Nutrition educators need such information on the processes used to change
dietitians’ behaviors to improve intervention efforts when targeted by stage of readiness
to eat fruits and vegetables.

The main purpose of this study was to develop a reliable process of change
questionnaire for eating adequate fruits and for eating adequate vegetables. A secondary
purpose was to examine the relationship between people’s stages of change and their
concurrent use of processes of change. A convenience sample of collegiate young adults

-was used for these purposes, because young adults were the target population of interest

for ﬁiture studies.

62

C. METHODS

Three steps comprised the process of instrument development to assess processes
of change for eating enough fruits and vegetables. We identiﬁed and reﬁned the process
items and then examined the relation between the process items and Stages of Change.
Step 1. To identify processes of change for eating enough fruits and vegetables.

The processes of change items for eating adequate fruits and vegetables from 11
categories were adapted ﬁrst from other studies (Prochaska et al., 1988; Prochaska et al.,
1992b; Bowen et al., 1994). Items were also developed from transcripts of interviews for
another study with young adults about eating fruits and vegetables (Betts et al., 2000).
Each fruit and vegetable process item had a 5-point Likert response scale ﬁ'om “never
use” to “always use” for the question “How frequently do you usually eat. . . ?”

Using the items drafted, processes of change items were sent to 12 outside experts
for review. Procedures for this group of experts included two activities. First, the experts
listed their own strategies or thoughts about eating fruits and vegetables and categorized
these processes. Secondly, the experts reviewed the draft of processes adapted from
earlier research and described in the preceding paragraph. Experts checked the items for
wording, sentence clarity and goodness of ﬁt in each category. Items were revised aﬁer
evaluation of the experts’ feedback. The major concerns expressed by expert reviewers
were whether processes of change were for increasing fruit and vegetable intakes or for
eating e_ngugl_1 fruits and vegetables. We decided to focus on eating ml; fruits and

vegetables because the staging instrument was designed to discriminate people who ate at

 

least the recommended number of servings of hit and vegetable from those who did not.

63

Because ‘enough’ is a relative term, it was used with the deﬁnitions explicit on the
instrument, i.e., eating at least 2 serving of fruits and at least 3 servings of vegetables.

Next, four focus group interviews (n=24) were conducted with collegiate adults
aged 18-24 years to clarify the items and to elucidate additional process items. First, I
focus group interviewers were recruited from an undergraduate nutrition class. The four
interviewers and focus groups were matched by race and gendernAfrican American
male, African American female, white male and white female. Interviewers attended a 1
hour training session on focus group techniques and were paid $100.00 for their total
time. Focus group interviews were conducted following standard methods (Stewart and
Shamdasani, 1990; Betts et al., 1996). The interviews, lasting from 60-90 minutes, were
audio tape-recorded. Each interviewer had ﬁve focus group attendees and one note taker.
Attendees were paid $10 each for participation.

Generally, most respondents in focus group interviews mentioned eating fruits
and vegetables as a substitute for unhealthy foods, keeping ﬁ'uits and vegetables readily
available and their mothers’ support for eating healthy foods like fruits and vegetables.
Most participants also reported that social relationships with friends were not relevant to
eating fruits and vegetables. Although our main purpose for focus group interviews was
to elicit the processes collegiate young adults use to eat enough fruits and vegetables and
to clarify items on the draft process instrument, most young adults told us the reasons
why they did or did not eat fruits and vegetables. The major reasons reported for eating
ﬁ'uits were that they liked the taste and the convenience. Subjects living in residence
halls answered they ate him and vegetables because they were available in the cafeteria.

Most females responded they ate fruits and vegetables as substitutes for unhealthy food

64

and that they felt healthy when they ate fruits and vegetables. Cited frequently by
students as important reasons for eating ﬁuits and vegetables were mother’s support for
eating healthy foods and keeping ﬁ'uits and vegetables available. Eighty process items

resulted from analysis of the focus group results.

Step 2. To reﬁne the process items.

Eighty items from 11 constructs (categories) were tested on a convenience sample
of 151 students (82% female) in an introductory nutrition class at a large, midwestem
university in the fall, 1999. The constructs at this step included consciousness raising,
dramatic relief, self reevaluation, social liberation, environmental reevaluation, self
liberation, counter conditioning, stimulus control, contingency management,
interpersonal control and helping relationships. A $1.00 coupon for redemption at a local
campus-run snack shop was given to students for returning questionnaires and consent
forms. To reﬁne the instrument, conﬁrmatory factor analysis (CFA) was used, because
we started with known constructs (Hunter and Hamilton, 1992). Cronbach’s cc
coefﬁcients for the ﬁnal constructs were calculated for reliability.

Conﬁrmatory factor analysis (CF A), which examines whether data are consistent
with a theoretical model to be identiﬁed, was chosen because process of change items
were adapted from 11 theoretical constructs (Prochaska et al., 1992b). CFA approach
assumes that constructs are inter-correlated, which is more likely to be true (Maruyama,
1998). Using r-—0.90 for inter-correlation between constructs as the cut point to combine
constructs, an instrument was developed for processes of change with 29 items for seven
constructs and this instrument testedlin Step 3. Inter-correlations of ﬁnal seven

constructs ranged from 0.21 to 0.83 for fruits and from 0.27 to 0.87 for vegetables.

65

Step 3. To examine how processes of change relate to stages of change.

The relationship between stages of change and processes of change Were examined
using data from an introductory nutrition class in spring, 2000. Response rate was 51%
among 700 students, who were given extra points toward their grade as an incentive.
Final analyses were done with 294 subjects, after excluding those with incomplete data.
Subjects were 80% female and 86% white, reﬂecting the demographic distribution of the
course.

The differences in ﬁequency of use of the seven processes of change by stage of
readiness to eat ﬁuits and vegetables were tested using ANOVA., followed by Tukey’s
multiple comparison tests. Processes of change scores from the seven constructs were
standardized to T-scores to put them on a comparable metric (mean=50, standard
deviation=10).

Stages for ﬁ'uit intake and stages for vegetable intake were measured separately.
Current fruit/vegetable consumption was assessed as a stage outcome to assign subjects
“pre” versus “post” action stages using self-rated intake, “How many servings of
fruits/vegetables do you eat a day?” Fruit intake included fruit and mu juice and
vegetable intake included fried potatoes and vegetable juice. Subjects were classiﬁed in
post action stages of Action or Maintenance, if they answered eating 22 servings for
fruits or _>_3 servings for vegetables. These outcome criteria were from the minimum
recommended intake in Food Guide Pyramid. Then, intention and time period for eating

enough ﬁ'uits/vegetables were asked for ﬁirther stage divisions (Glanz et al., 1994; Hoerr

et al., 1997).

66

People who had eaten 22 fruits/_>_3 vegetables more than six months were assigned
as in maintenance stage and those for less than six months were assigned as in action
stage. The subjects who did not meet the outcome criteria were assigned into a pre-action
stage of precontemplation, contemplation or preparation. Respondents were classiﬁed
into precontemplation when they had no intention to eat 22 for ﬁuits or 23 for
vegetables, into contemplation when they intended to eat these within 6 months, and into
preparation, when they intended to eat these within 30 days (Figure 1).

Of the people in pre-action stages (n=99 fruit, n=l70 vegetable) we further
separated those who were there because of “relapsing”, so we could examine if they used
unique procesSes. For separating relapsers from others in pre—action stages, a question
was asked if their past experience of increasing fruit and vegetable intake was successful.
Those who answered “No” were assigned into the relapser group and the remainder in
pre-action stages were classiﬁed as “pre-action — R”. For those in maintenance, a
question about whether people had ever tried to increase fruit and vegetable intakes was
used to divide those in maintenance by changing their behavior change from those who
were in maintenance just by habit (Figure 1). People in maintenance by habit have not
been separated from maintenance when the TTM model was used in other dietary studies
(Green et al., 1994; Brug et al., 1997). However, because people who had never smoked
were excluded from the Stage of Change study for smoking cessation, we thought people
who had eaten adequate fruit and vegetable habitually, but not by intentions, should be
examined separately. It would be important to see, especially in this examination of
processes of change, if there were differences in people who cognitively consumed

enough fruits and vegetables versus those who did so by habit. The uses of processes of

67

change were compared between two groups, “relapsers” versus “pre-action — R” and

“maintenance by change” versus “maintenance by habit” using student independent t—test.

D. RESULTS _
Results of Step to identify the processes of change for fruits and vegetables were

reported in the Methods but also here with Step 2 for the results of the factor analysis.
Data relating to the second purpose are reported in Step 3.
Steps 1 & 2. Identiﬂing and reﬁning the processes of changes.

Table 1 shows the conﬁrmatory factor loadings and the coefficient alphas of each
construct, which ranged from 0.69 to 0.84, i.e. acceptable for these 3 to 6-item scales.
The same process items were chosen for both hits and for vegetables which had the
highest reliabilities. The three original constructs “Consciousness Raising, Dramatic
Relief and Environmental Reevaluation” were combined into one construct “Health
Concerns” due to high correlation (>090) among constructs both for fruits and for
vegetables. “Self Liberation, Stimulus Control and Counter Conditioning” were likewise
combined into the construct “Health ‘Commitment/ActiOn”. Several items with the
concept of self rewards for behavior change, originally from contingency management,
were moved into “Self Reevaluation”. Process items related to rewards from others in
contingency management were labeled as “External Reinforcement”. Twenty-nine items
for seven constructs resulted: “Health Concerns, Self Reevaluation, Social Liberation,
Health Commitment/Action, Interpersonal Control, External Reinforcement and Helping
Relationship”. The Hunter and Hamilton software (1992) allows one to test the internal
' consistency of each of the seven scales associated with the seven constructs. For all

constructs for fruits and of vegetables, except for health commitment/action for

68

vegetables, the value of Chi Square was not signiﬁcant (p>0.05), indicating a good ﬁt to
the measurement model. Therefore, for this reason and to reduce respondent burden, the
same items for the same constructs were kept for both fruits and vegetables.

Step 3. How processes of change relate to stages of change.

Using the original ﬁve stages, people in precontemplation used less “Self
reevaluation” and “Health commitment/action” than those in post action stages of fruit
intake (p<0.001). For vegetable intake, only people in pre-action used less “Health
commitment/action” than those in maintenance stage (Data not shown).

Comparing the processes of “relapsers” versus “pre-action - R”, there was no
difference on any processes of change for fruit. However, for vegetable intake, a higher
use of self-reevaluation processes, but lower use of health commitment process, resulted
for “relapsers” compared to those in “pre-action — R” (Figure 2). When we examined
processes of change between those in maintenance by change versus by habit, we found
no difference for ﬁuit, but did for vegetables. Those in maintenance by change who ate
enough vegetables used more processes for health concerns, self-reevaluation and health
commitment/action than those in maintenance by habit (Figure 3).

Table 2 shows T-scores on each process of change among stages of change further
separating relapsers and maintenance by habit from those in the ﬁve original stages.

People in precontemplation were least frequent users of processes of change compared to

those in other stages.

E. DISCUSSION

69

This is the ﬁrst study to examine comprehensively the process of change dimension
from the TTM for ﬁ'uits and vegetables. Findings for Steps 1 and 2 demonstrate that
young adults have unique processes of change and that these differ for eating fruits and
for eating vegetables. Young adults likely have fewer processes for eating fruits and
eating vegetables than do older adults. Investigators who examined process of change for
fat consumption found 10 processes with middle-age adults, whereas we found only
seven for fruits and vegetables with young adults (Ounpuu et al., 2000). Findings for I
Step 3 suggested that the processes of self reevaluation and health commitment are
appropriate targets both for fruits and for vegetables, and health concerns, only for
vegetables.

Step 1 & 2. Identifying and reﬁning processes.

The construct “Health concerns” combined consciousness raising, dramatic relief
and environmental reevaluation processes, because young adults did not discriminate
among those for eating enough fruits and vegetables. Prochaska and colleagues
identiﬁed these three original processes as important ones for people to move from
precontemplation to contemplation in smoking cessation (Prochaska et al., 1992a).
Results from studies on low fat intake with middle age people did not combine those
three constructs either (Bowen et 1a., 1994; Prochaska et al., 1988; Ounpuu et al., 2000).
Therefore, another possible reason for failure to discriminate among the three is these
young adults were less aware of the importance of eating fruits and vegetables compared
to older adults.

Self liberation, stimulus control and counter-conditioning were also combined into

one construct — “Health commitment/action” - in this study, due to high correlation

70

between constructs. This collapse of earlier constructs may be because eating fruits and
eating vegetables are behaviors promoted or substituted, not avoided, like smoking. To
make a health commitment, fruits and vegetables should be available in the near
environment rather than removed from the environment as for smoking cessation.

There were few references to social support processes observed in our focus
groups, when young adults were asked what they did to eat enough fruits and vegetables.
Most responded that “social inﬂuences”, except for their mothers’ support, were not
important to them for eating fruits and vegetables. However, in a previous study, young
adults reported that the food they ate was affected by what their friends ate (Betts et 1a.,
1997). It might be that at this age friends inﬂuence the consumption of some foods, but
not especially that of fruits and vegetables. This can be true, even though young adults in

college have been reported to have more healthful dietary habits than those who had

never been to college (Georgiou et al., 1997).

Step 3. How process of change relate to stages of change.

Although items developed for processes of change were the same for eating fruit
and for eating vegetables, several different aspects for each were found in relation to
stage of change. One of the differences was that the relationship between stages and use
of the process “health concerns”, differed signiﬁcantly among stages only for vegetables.
Two processes common to both fruits and vegetables, which differed among stages, were
self reevaluation and health commitment/action. These ﬁndings are in contrast to those
ﬁ'om smoking cessation, where the use of all 10 processes, except social liberation,

differed signiﬁcantly among stages (DiClemente et al., 1991). In this young adult

71

population, only self reevaluation and health commitment/action for fruits and vegetables
and only health concerns for vegetables differed signiﬁcantly among stages.

The use of self reevaluation by those in precontemplation was lower than by those
in post-actions stages for both hit and vegetable intake. This ﬁnding is similar to those
for smoking cessation, although our self reevaluation process included a self reward item,
“I feel good about myself when I eat enough” (Prochaska and DiClemente, 1984b;
Prochaska et al., 1992a). If supported by longitudinal studies, it might be possible to use
self reevaluation to help people move from precontemplation to preparation and action
for fruits and vegetables.

The pattern of use of health commitment processes differed between eating fruit
and eating vegetables. For vegetables, health commitment differed only between
“maintenance by change” and other stages, whereas, health commitment differed among
several stages for fruit. Health concerns for vegetable intake had a signiﬁcant
relationship with stages, but not for fruit intake. Therefore, processes for eating enough
ﬁuit may not relate to health concerns for these young adults. A study on social support
with fruit and vegetable intake combined reported that coworkers and household supports
were signiﬁcantly associated with stages of change in that population group (S orensen et
al., 1998). However, our ﬁndings did not support those results, because the processes
related to social support—such as social liberation, interpersonal control, external
reinforcement and helping relationship--did not differ signiﬁcantly among stages for
either fruits or vegetables.

When adapting Stage of Change Theory from use for smoking cessation to

changing dietary practices, two important factors often have been overlooked. The ﬁrst

72

is that people who had never smoked in the past were not included in the research on
smoking cessation In fact, those people not smoking, because they never did, need no
processes at all for not smoking. Therefore, if somebody had eaten adequately just as a
habit without having tried to change their diet, that person might not need any special
processes or methods to eat adequately. Most stage of change studies have not addressed
this issue, but the study of process of change should. We want to identify the processes
used by those who have cognitively changed their dietary habits. Glanz and collegues
(1994) did consider past experience for changing dietary behavior, but only to assign
people into pre-action stages.

Another important difference from smoking cessation in this study is how
“relapser” is deﬁned. People who have had an unsuccessﬁrl experience with changing a
particular behavior likely think and behave differently from people currently trying to
change. Studies for smoking cessation have reported a clear pattern of processes used by
people across each of the ﬁve stages, when relapsers were removed (Prochaska et al.,
1991; Prochaska et al., 1994). Our ﬁndings showed “relapsers” had higher use of self
reevaluation and lower health commitment/action processes than did people in pre action
stages. We interpret this to mean that relapsers think a lot about eating fruits or
vegetables, but do not commit. If so, this has implications for nutrition educators in that
we need to ﬁnd ways to help relapsers commit to making small changes in their diets.

The “maintenance group by change” had higher scores than “maintenance by
habit” for the process of health concerns, self reevaluation and health commitment/action
for eating vegetables. This could mean that if people think they have eaten enough

vegetables, without having had the experience of trying to do so, they do not need many

73

processes to change or maintain intake. Surprisingly, for eating ﬁuit, no processes
differed between maintenance by habit and maintenance by change. The reason for this
is unknown, and perhaps, the processes for eating fruit might be more affected by other
factors such as intention, perceived current intake and availability, rather than by past
experience when compared to processes for eating vegetables. Another possibility is that

we have not adequately captured important processes for eating enough ﬁuit.

Strengths and limitations.

The ﬁndings in this study are likely generalizable to young adult college women.
Because the use of processes of change can differ by health behavior and demographics,
further work is necessary to explore processes of change for men and for limited income
groups. These ﬁndings need to be repeated with a larger number of people, because a
small number in certain stages can lead to loss of statistical power to detect differences
where they exist. Furthermore, longitudinal studies are necessary to ﬁnd the true changes
in use of processes for changing behaviors and to identify more detailed information
about people assigned to speciﬁc stages, including relapsers or people in maintenance.

one of the strengths of this study was the separation of eating ﬁuits from eating
vegetables for all instruments. Our data clearly support the different processes for eating

enough fruits and eating enough vegetables.

F. IMPLICATIONS FOR RESEARCH AND PRACTICE
Nutrition educators should consider that young adults are much less aware of the
health beneﬁts of fruits and vegetables compared to older adults. Our ﬁndings suggest

that for those in pre-action stages some strategies nutrition educators should try with

74

young adults include self-evaluation of their fruit and vegetable intake. Other processes
to promote adequate fruit and vegetable intakes include use self rewards such as feeling
healthy and keeping ﬁuits and vegetable around to substitute for high fat foods. Health
concerns would be a good strategy to promote adequate vegetable intakes, but not fruit
intakes. People who are relapsers and those in maintenance by habit should be identiﬁed,
sonutrition educators can develop and apply effective intervention techniques. It is
important to identify the processes of change matched to each stage to develop effective

intervention techniques to help people eat enough fruits and vegetables for good health.

75

 

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*Signiﬁcant diﬂ‘erent between groups (p<0.05)

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80

Chapter Five
Self-efficacy and health behaviors are associated with young adults’ fruit and

vegetable consumption

A. ABSTRACT

Background This study is to determine the self-efﬁcacy, demographics, psychosocial
factors and health behaviors associated with eating inadequate amounts of fruits and
vegetables in young adults.

Method Demographics, psychosocial factors and mu and vegetable intakes from a
three-day food record were collected from 294 college students. Less than 2 servings of
total ﬁ'uits, or fruits excluding juice, and less than 3 servings of total vegetables, or
vegetables excluding fried potatoes, were used as inadequate intakes to conduct
multivariate logistic regressions controlling for gender, race, residency and energy intake.
Results. F iﬁy-eight and 82% of the respondents reported inadequate total fruits or fruits
excluding juice, respectively. Fifty-three and 63% reported inadequate total vegetables
or vegetables without ﬁ'ied potatoes. College students who were female, non-smokers,
lived in a campus residence hall, ate breakfast more often, and exercised regularly, were
less likely to eat inadequate amounts of fruits than those who were not. Successful
experiences with increasing fruits and past experience with increasing vegetables,
regardless of success, were positive predictors for fruit and for vegetable intakes,
excluding fried potatoes. Self-efﬁcacy was inversely associated with risk for inadequate
consumption of both fruits and vegetables. Increasing discretionary fat consumption was

associated with an increased risk for inadequate ﬁ'uit intakes, but not vegetable intakes.

81

Conclusion Eating ﬁ'uits is associated more with other healthy behaviors than is eating
vegetables in young adults. Only self-efﬁcacy was positively associated with both ﬁ’uit
and vegetable intakes.

Key Words: fruits; vegetables; healthy behaviors, self-efﬁcacy, fat

B. INTRODUCTION

Due to the health beneﬁts associated with fruits and vegetables (Steinmetz and
Potter, 1996; Ness and Powles, 1997), eating two or more servings of fruits and three or
more servings of vegetables a day has been a public health recommendation in the Food
Guide Pyramid, the Dietary Guidelines, Healthy People 2010 and the Five-A-Day
promotion (USDA & USDHHS, 1992; USDA & USDHHS, 1990; National Research
Council, 1989; U.S. Department of Health and Human Services, 1991; Subar et al.,
1995). Several studies, however, have reported that actual fruit and vegetable
consumption in the United States is considerably less than that recommendation (Krebs-
Smith et al., 1995a; Krebs-Smith et al., 1996). Dietary data from 8181 adults (>20 yr
old) in the USDA’s 1989-1991 Continuing Surveys of Food Intakes by Individuals
(CSFH) over three days showed only 32% met the objective of ﬁve or more servings of
hits and vegetables per day (Krebs-Smith et al., 1995a). Assessments of the 3148
children and adolescents from the 1989-1991 CFSII data demonstrated only 20% ate
more than ﬁve servings of fruits and vegetables (Krebs-Smith et al., 1996).

Early practice of sound dietary habits in young adulthood is associated with

reduced risk for chronic disease later in life (Raitakari et al., 1994). Young adults in

college are in a transitional period between living and eating at home and living on their

82

own and feeding themselves (Lau et al., 1990). Furthermore, young adults have the
necessary mental processing equipment to consider future health risks while
preadolescent children are less likely to do so (Domel et al., 1996). Therefore, efforts to
establish well-founded health habits, including dietary behaviors, during this time of life
should have positive long-term health consequences. Although the importance of
deve10ping healthy dietary habits at this time has been emphasized, young adults’ intakes
of fruits and vegetables remain low (Georgiou et al., 1997).

Knowing which factors are particularly associated with inadequate intakes of fruits
and of vegetables in speciﬁc populations can be useful to target interventions to increase
intakes of fruits and vegetables. Although several studies about dietary behaviors,
including some on ﬁ'uit and vegetable consumption, have been done with college
populations (Betts et al., 1997; Keim et al., 1997), little has focused on factors associated
with inadequate fruit and vegetable intakes.

There have been a few studies in several populations on determinants of
psychosocial factors related to eating fruits and vegetables. A study of attitudes toward
ﬁ‘uit and vegetable consumption in a population from the Special Supplemental Food
Program for Women, Infants, and Children (WIC), for example, showed positive
perceptions of ﬁ'uits and vegetables were important to intakes (Treiman et al., 1996).
Investigators also reported barriers to increasing consumption, such as a lack of
availability, the time and effort to prepare fruits and vegetables, and preferences for other
foods. In a study of randomly sampled Washington State residents, nutrition behavior
scores largely depended on barriers to fruit and vegetable intakes. In the Washington

study, elements of the Health Belief Model -- including beneﬁts of and barriers to fruit

83

and vegetable intakes, and susceptibility to cancer and nutrition concerns -- explained
16% of the variance of fruit and vegetable intakes (Dittus et al., 1995). In another study
with adolescents, inadequate fruit and vegetable intakes were associated with low
socioeconomic status, low family connectedness, weight dissatisfaction and poor
academic achievement (Neumark-Sztainer et al., 1996). In studies of Dutch adults, self-
efﬁcacy was signiﬁcantly associated with consumption of cooked vegetables, salads or
fruits (Brug et al., 1995; Lechner et al., 1997). Studies by Brug and Lechner separated
vegetables into sub-categories, such as boiled vegetables and salads, because the
researchers proposed different patterns of psychosocial factors associated with these sub-
categories of vegetables. In fact, due to high fat content, fried potatoes have often been
excluded from vegetable consumption in studies, including the S-A-Day National
research program (Calvert et al., 1997; Smith-Warner et al., 1997; Serdula et al., 1993;
Thompson et al., 1999). Although fruit juice was included in the S-A-Day studies, it can
4 be viewed more as a beverage than a fruit by consumers and thus its intake might be
associated more with different psychosocial factors than whole fruits.

In order to help plan effective interventions, the purpose of this study was to
identify relationships between inadequate hit and vegetable intakes and concomitant
psychosocial factors in collegiate young adults. Furthermore, such possible associations

were examined by separating fruit juice from fruits and fried potatoes from vegetables.

84

C. METHODS

Subjects

A convenience sample of subjects aged 18-24 years was recruited from two introductory
nutrition classes at a large, north central land grant university during the winter. A total
of 360 subjects (response rate=51%) completed the survey at the baseline. Subjects with
incomplete dietary data (n=66) were excluded, as were 44 subjects with only two days of
records. Data were usable from 294 people. I

Procedure

After receiving approval from the University Committee on Research Involving
Human Subjects, consent forms and a set of instruments about ﬁ'uits and vegetables were
distributed to interested participants. The instruments included demographics,
psychosocial factors associated with fruit and vegetable'intakes and 3 days of food
records. Subjects reported three days of food intake for two weekdays and one weekend
day. Detailed instructions for recording food intakes were provided to increase the
accuracy of the records. Subjects were instructed to report all the food they ate. As an
incentive, extra points toward class grades were given for complete questionnaires.
Instruments '

Demographics

Respondents were asked to report their gender, place of residence, race/ethnicity,
nutritional supplement use, smoking, drinking, physical activity, volunteer activities and
employment. Subjects could choose from the following race/ethnic groups: White,
Black, Hispanic, American Indian/Alaska Native, Asian/Paciﬁc Islander and other.
Supplement use, smoking, drinking, regular physical activity, volunteer activities and

employment were dichotomous variables answered as yes or no.

85

Psychosocial factors

Weight satisfaction was measured using the ﬁve-point Likert scale from “very
satisﬁed” to “very unsatisﬁed”. “Very satisﬁed” and “satisﬁed” were coded as
“satisﬁed” and “very unsatisﬁed” and “unsatisﬁed” were as “unsatisﬁed”.

Instruments for decisional balance and self-efﬁcacy developed by a ten-state
research project team for use with young adults were used (Betts et al., 2000). Decisional
balance consists of 18 items including 10 cons (perceived barriers) and 8 pros (perceived
beneﬁts) items. Items for decisional balance included external motivation/barriers, health
concerns, weight control and other factors. A ﬁve-point Likert scale, ranging from “not
at all important” to “very important” was used for decisional balance. In this study,
Cronbach’s or for pros was 0.73 for fruits and 0.72 for vegetables. For cons, 0.71 was for
fruits and 0.72 was for vegetables.

Self-efﬁcacy to eat the recommended serving number of ﬁ'uits and vegetables was
asked using 5 items with 5 point Likert responses from “not at all conﬁdent” to ‘Very
conﬁdent”. Cronbach’s or for self-efﬁcacy items was 0.79 for fruits and 0.77 for
vegetables.

To analyze the psychosocial factors, standardized T-scores were calculated for the
pros (perceived beneﬁts), the cons (perceived barriers) and self-efﬁcacy (mean-50, SD-
10). This transformation resulted in comparable scores correcting for different levels of
dispersion among the variables.

Food Records - Fruit and vegetable servings

To assess past experience with changing ﬁ'uit and vegetable consumption behaviors,

subjects were asked, “Have you tried to increase your ﬁ'uit/vegetable intake?” Ifsubjects

86

answered “yes”, they were asked if their efforts were successful. All questions were
asked for fruits and vegetables separately.

From the three-day food records, average servings of food groups and discretionary
fat were calculated based on the database from the 1994-96 USDA Continuing Survey of
Food Intakes by Individuals (CSFII) (U .S. Department of Agriculture, 1998). The
Expanded Food and Nutrition Education Program (EFNEP) Evaluation/Reporting System
(ERS) (U .S. Department of Agriculture, 1994) was selected as the nutrition software.

The ﬁrst author supervised dietetic students who performed the dietary data entry and
checking with the ERS software. This EFNEP software can calculate food group
servings, energy intake and nutrients. The EFNEP Evaluation/Reporting System (ERS)
has an accessible database for corrections and results can be exported easily for further
statistical analysis. Because the ERS was developed prior to the release of the CSFH,
some discrepancies were found between the servings in the CSFII and those in the ERS.
Therefore, the database of the ERS for foods counting ﬁ'uits and vegetables was revised
using the Microsoft Access program using the CSF H servings as the standard. For the
ERS revision, ﬁrst the USDA food 'code for each food in the ERS was recorded using
Codebook Search (CBSRCH) in the 1994-96 CSFH CD-Rom‘ to match the food names
between the ERS and the CSFH. Then, the CSFII food-serving database was searched by
USDA food code._ Food servings per unit of food were calculated, because the CSFII
serving database used 100g of food. Ifthe weight per unit of food in the ERS differed
from the CSFII, the CSFII weight of food was selected. Some foods in the ERS could
not be matched to foods in the CSFII. In that case, a CSFH food was matched to a food

based on an equivalent or similar composition of ingredients.

87

Analysis

The Statistical Package for Social Science (version 7.5 for Windows) was used for
data analysis. Logistic regressions for the odds ratio of fruit and vegetable intakes were
run separately, coding fruit and vegetable consumption, the dependent variables, as
“adequate” or “ inadequate”. Inadequate intake for fruits was less than two servings; for
vegetables, three servings. Separate logistic regressions were conducted for two servings
of hits, excluding ﬁ'uit juice, and three servings of vegetables, excluding fried potatoes.
An odds ratio of 1.00 indicated no association, if the value 1.00 was included within the
95% conﬁdence interval, i.e., the association was not signiﬁcant (p<0.05). The
magnitude of deviation from 1.00 in either direction shows the strength of the
association, with the direction from 1.00 depending upon the reference group.

Energy intake was adjusted for all the demographic information due to signiﬁcant
relationships between ﬁ‘uit and vegetable servings and energy intake. Logistic
regressions for psychosocial factors and food groups were conducted controlling for
energy intake, gender, race and residency to exclude the effects of those factors. Odds
ratios were calculated using a standardized score increment of 10 for the psychosocial
factors, using 1 serving increments for each food group, and using increments of 10

grams of discretionary fat.

D. RESULTS

The characteristics of the participants are presented in Table 1. Eighty percent of
the subjects were female and 86% were white. Sixty-seven percent of the females lived
in campus residence balls. The percent of residency and weight satisfaction differed

signiﬁcantly between men and women by Chi-square test. Women were more likely to

88

live in residence halls and were not more likely to be satisﬁed with their own weight in
this population. Table 2 presents, the mean scores and standard deviations of
psychosocial factors before standardization, and of the food group intakes by gender.
Reported intakes of all food groups, except total fruits and hits without ﬁ'uit juice,
differed between male and female by independent t-test (p<0.001). Discretionary fat and
energy intake was also higher in males. However, when energy intake was adjusted,
signiﬁcant differences by gender remained only for intakes of meat, total fruits and fruits
excluding ﬁ'uit juice.

Percentages of respondents reporting less than two servings of total fruit and of
ﬁ'uit excluding fruit juice consumption ﬁom a three-day food record were 58% and 82%,
respectively. Likewise, 53% reported less than three servings of total vegetables and
63% reported less than three servings of vegetables excluding ﬂied potatoes. Table 3
represents the odds ratios for less than two servings of hits with and without juice and
for less than three servings of vegetables with and without ﬁied potatoes when energy
intake was adjusted. Race, drinking alcohol, vitamin/mineral supplement use and weight
satisfaction were not associated with either fruit or vegetable consumption. Gender was
associated with fruit consumption, with females at a lower risk for inadequate total fruit
consumption (OR=0.30, p<0.001). Those living in residence halls were at a lower risk '
for less than two servings of ﬁ'uit consumption either with or without juice (OR=0.36,
OR=0.40, p<0.001). Because women were more likely to live in residence halls in this
population, the odds ratio for gender was ﬁrrther adjusted by residency. Then, gender
was still associated with inadequate total fruit intake, but not with inadequate ﬁ'uit

without juice intake (data not shown in Table 3). Smoking was negatively associated and

89

physical activity was positively associated with eating total fruits and fruits without juice.
Eating breakfast one more day of the week was associated with an 18% lower risk for
less than two servings of total ﬁ'uits and of fruits without juice. Successful experience for
increasing fruits had the lowest odds ratio for less than two servings of fruits with juice or
without juice. For vegetables, only past experience for increasing vegetables was
signiﬁcantly associated with vegetables without fried potato consumption.

The odds ratios for the effects of psychosocial factors and intake of other food
groups on fruits and vegetables are in Table 4. Only self-efﬁcacy was positively
associated with both fruit and vegetable intakes. Ten standardized score increments of
perceived beneﬁts (pros) and self-efﬁcacy was signiﬁcantly associated with a 38% and
45% lower risk for inadequate fruit consumption and a 37% and 59% lower risk for less
than two servings of fruits without juice, respectively. Perceived barriers were negatively
associated with fruit intake without juice. For vegetables, self-efficacy was associated
with both total vegetables and vegetables without ﬁied potatoes, and pros were associated
with only vegetables without fried potatoes.

Increments of 10g discretionary fat were associated with an increased risk for
eating less than two servings of fruits, either with or without juice. Eating more servings
of fried potatoes showed an increased risk for inadequate fruit intakes, whether ﬁ'uit juice
was included or not. Drinking more juice also had an association with increased risk for
inadequate vegetable intakes, but only when ﬁied potatoes were separated. Although
total ﬁ'uit intake was not associated with vegetable intake without fried potatoes, and total
vegetable intake was not associated with fruit intake, consuming more fruits without juice

was associated with increased vegetable intake excluding ﬁ'ied potatoes.

9O

E. DISCUSSION
These ﬁndings demonstrated that self-efﬁcacy had the strongest positive association

with both fruit and vegetable intakes, whether or not juice or fried potatoes were
included, regardless of race, gender, energy intake and place of residence. The pros and
cons from decisional balance were also associated with ﬁ'uit and vegetable intakes,
although not as strongly as self-efficacy. Several other investigators have reported
similar associations of self-efficacy with ﬁ'uit and vegetable intakes in different
populations (Brug et al., 1995; Dittus et al., 1995; Havas et al., 1998; Baranowski et al.,
1999), making self-efficacy for fruits and vegetables an important concept for health
professionals to address with clients. Dittus and colleagues found perceived barriers
from the Health Belief Model (HBM) to be the strongest predictor of fruit and vegetable
intakes (Dittus et al., 1995). However, cons were associated only with fruits without
juice in this study.

In the present study, perceived beneﬁts (pros) were associated with fruit intake
with or without juice, but only with vegetables when fried potatoes were excluded. This
might suppert somewhat different patterns of psychosocial factors for certain sub—
categories of fruits and vegetables, because fruit juice can also be considered as a
beverage, and fried potatoes as a high fat food. The results of food group analysis also
supported this different pattern of fruit juice and fried potatoes, which might reﬂect
consumers’ differing perceptions of them.

Another noticeable ﬁnding from this study was that the behavior of eating fruits
was more associated with other healthy behaviors compared to the behavior of eating

vegetables. Living in residence halls, not smoking, exercising regularly, and eating

91

breakfast and less discretionary fat were signiﬁcantly associated with only fruit
consumption, but not with vegetable consumption Other studies with adolescents have
found gender differences for inadequate intakes of fruit and vegetable, as we did, but
without adjusting for total energy (N eumark-Sztainer et al., 1996; Story et al., 1998).
Our data showed that living in campus residence halls might be beneﬁcial for eating
adequate fruits. Vegetable intake, however, was not affected by place of residence.

Smoking and regular exercise were related to only ﬁ'uit consumption. Another
study also demonstrated that exercisers more ﬁ'equently met the Food Guide Pyramid
recommended fruit intakes than non-exercisers (Georgiou et al., 1996). Similarly,
Neumark-Sztainer and colleagues reported smoking and drinking alcohol were related to
both inadequate fruit and vegetable consumption in adolescents (N eumark-Sztainer et al.,
1996). In this present study with young adults, however, eating vegetables was not
related to other healthy behaviors.

Eating breakfast has often been reported to relate to other healthy dietary behaviors
such as low fat intake (Schlundt et al., 1992; Huang et al., 1997). Our ﬁndings supported
an associatiOn of breakfast eating with adequate fruit consumption. One more day of
eating breakfast was associated with decreasing the risk of eating less than two servings
of fruits, with or without juice. Added discretionary fat was negatively associated only
with fruit consumption. Billson and colleagues also showed a negative association
between fruit and vegetable consumption and “other fat” using quintile groups of fruits
and vegetables (Billson et al., 1999). The investigators did not indicate whether ﬁ'uits or
vegetables or both were related to “other fat”, because they used combined hit and

vegetable consumption for analysis.

92

Past experience to increase vegetable intake and past successﬁil experience to
increase fruit intake were strong predictors for the consumption of vegetables and fruits.
This result for vegetables was signiﬁcant only when fried potatoes were excluded. Just
trying to increase intakes of vegetables or fruits seems to be good indicators for eating the
recommended number of servings. The importance of past success to dietary change has
also been reported in a study about fat and ﬁber (Glanz et al., 1993).

D A major strength of this study was the separation of fruits and vegetables in the
examination of factors predicting their intake. Findings imply that different intervention
strategies are needed to promote eating fruits or eating vegetables.

The use of three-day food record from which to estimate dietary intake was both a
strength and limitation in this study. Three days of total intake, a large respondent
burden, is a larger number of days than even national surveys are now using, but a
smaller number than some report as necessary to capture usual intake of fruits and
vegetables (Baranowski et al., 1997). Likewise, subject selection bias due to this high
respondent burden might have also affected the results.

Due to using a convenience sampling from a nutrition class with mostly white
women, we cannot generalize our results to all college students. Several studies for
inadequate fruit and vegetable consumption have supported differences by ethnic group
(N eumark-Sztainer et al, 1996; Havas et al., 1998). To verify these ﬁndings with other
demographic groups, further studies are needed.

In conclusion, health behaviors such as not smoking, regular exercise and frequent
breakfast eating were associated with fruit consumption. Less discretionary fat

consumption was related to only ﬁuit consumption. Future studies of ﬁ’uit and vegetable

93

dietary behaviors should consider the sub-categories of ﬁ'uit juice and fried potatoes

separated from total fruits and vegetables.

94

Table 1. Percentage of participants by demographics and health behaviors

 

 

Variables Men (n=5 8) Women (n=23 6)
Race '
White 89.7 84.7
Non-white 1 0. 3 1 5.3
Residency"
Residence halls 48.3 66.9
Others 51.7 33.1
Smoking
Yes 12.1 17.5
No 87.9 82.5
Alcohol drinking
Yes 77.6 66.4
No 22.4 33.6
Exercising regularly
Yes 65.5 65.0
No 34.5 35.0
Employment or volunteer activity
Yes 55.2 63.6
No 44.8 36.4
Vitamin/mineral supplement use
Yes 54.4 41.7
No 45.6 58.3
Weight satisfaction***
Satisﬁed 69.4 37.9 '
Unsatisﬁed 30.6 62. 1
Past experience to increase fruit
Yes 74.1 75.7
Successful experience to increase fruit
Yes 62.8 69.3
No 37.2 30.7
No ' 25.9 24.3
Past experience to increase vegetable
Yes 58.6 64.0
Successful experience to increase vegetable
Yes 60.6 68.4
No 39.4 3 1.6
No 41.4 _ 36.0

 

** p<0.01, group difference by Chi-square
*** p<0.001, group difference by Chi-square

95

Table 2. Average of psychosocial factors, food group intakes and ﬁ'equency of breakfast
eating by gender (Mean : Standard deviation)

 

 

Psychological variablesI Men (n=5 8) Women (n=23 6)
Fruit
Pros (perceived beneﬁt)" 3.3 5:0.70 3.64:0.66
Cons (perceived barrier) 2.43:0.65 2.52:0.69
Self-efficacy 3 .7 1 :0. 77 3 .76:0. 82
Vegetable
Pros (perceived beneﬁt)" 3.33:7 . ll 3.63:0.65
Cons (perceived barrier) 2.46:0.65 2.56:0.69
Self-efﬁcacy 3.47:0.76 3.57:0.83
Food group (unit)2
Meat (1 oz Meat Equivalent(MLE))*** 6.04:3.54 3.14:2.02
Dairy (serving)*** 2.81:2.29 1.69:1.27
Bread (serving)*** 8.36:3.30 6.00:2.31
Total fruit (serving) 1.91:2.02 1.97:1.63
Fruit without juice (serving) 0.86:1.33 1.1 1 :1 . 12
Total vegetable (serving)*** 3.93:2.63 2.90:1.66
Vegetable without fried potato (serving)*** 3.19:2.63 2.53: 1 .50
Discretionary fat (gram *** 59.4:26.4 40.5:21.0
Energy intake (kcalorie)*** 2393:855 1629:524
Frequency of eating breakfast/week3 3 .6:2.3 3.4:2.3

 

1: Based on the average of 8 questions for pros, 11 questions for cons and 5 questions for self efﬁcacy,
each on a scale of 1 through 5. 1 - not at all important to 5 - very important to eat fruit/vegetable in pros and
cons, l — not at all conﬁdence to 5 - very conﬁdence to eat at least 2 servings of fruit/ 3 servings of
vegetable in self-efficacy. ‘

2: Calculated from 3-day food records based on Continuing Survey of Food Intake for Individual (CSFH)
food servings

3: 0-7 were answered as frequency of breakfast per week

** Signiﬁcantly different between male and female at p value <0.01, t-test.

*** Signiﬁcantly different between male and female at p value <0.001, t-test.

96

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98

Chapter Six
Report of Construct Validity by Three Staging Methods

This chapter was written as a report, not a manuscript, because it has
supplementary information about the validity of the Stages of Change Theory for eating
fruits and vegetables. Some of the ﬁndings related to validity were in Chapter Three and
some of the information related to processes of change was in Chapter Four.

The purpose of this report was to establish construct validity by comparing the
results of psychometric constructs with three staging methods tested for outcome
validity. The differences for decisional balance, self-efﬁcacy and processes of change by
the stages are reported by Method A (Self-rated intake), Method B (Recall) and Method
C (Food ﬁequency) from Chapter Three. The methods for assessing decisional balance
and self-efficacy were described in Chapter Five and the methods for processes of change

in Chapter Four.

A. Results

For fruit and vegetable intakes, Table 1 shows decisional balance (pros and cons)
and self-efﬁcacy across the ﬁve main stages of change plus stages for relapsers and those
in maintenance by habit. Self-efficacy differed among the stages for both eating fruits
and eating vegetables by all three methods (p<0.001), except Method B (24-hour recall)
for vegetables. As expected, people in precontemplation and relapser stages had the
lowest self-efﬁcacy scores and those in maintenance, either by change or by habit, had
the highest self-efﬁcacy scores for both ﬁuits and vegetables. For fruits, stages did not

differ by scores for pros and cons by any staging methods, except for pros by Method B.

99

For Method B which used a recall, the pros were higher for preparation, action and
maintenance compared to other stages. Only the scores for cons by staging Method C
(food frequency) showed signiﬁcant differences among the stages for vegetables. Those
in precontemplation and relapser stages had high scores for cons and those in
maintenance by change and by habit had low scores.

When the use of processes by stages was compared among three staging methods,
similar results for processes of change were found by all three staging methods for fruit
intake. However, for vegetables, use of processes by stages using Method B (24 hour
recall) showed different results ﬁ'om those using Method A (self-rated intake) and
Method C (food frequency).

Among the seven processes, mean scores for only two -- self-reevaluation and
health commitment/action -- differed signiﬁcantly across the stages for ﬁ'uits (Table 2).
For vegetables, mean scores for health concerns, self-reevaluation and health
commitment/action differed signiﬁcantly between stages by staging Method A (self-rated
intake) and Method C (food frequency). The uses of self reevaluation and health
commitment/action by those in precontemplation were lower than by those in post-
actions stages for both fruit and vegetable intake. By Method B (24-hour recall), only the
uses of health concerns and health commitment/action differed signiﬁcantly by the
stages.

For both fruits and vegetables, people who were relapsers tended to have high self-
reevaluation scores and maintenance by change had the highest health
commitment/action scores by all staging methods. For vegetables, relapsers showed high

scores for health concerns compared to those in other stages, but for fruits, they did not.

100

B. Discussion

These ﬁndings demonstrated that a certain amount of construct validity exists
between stages of change and psychosocial factors using all three staging methods for
fruits and vegetables, except for Method B (24 hour recall) for vegetables. For fruit, .
similar pattern of pros, cons, self-efficacy and processes of change across stages were
shown using all three staging methods. Therefore, staging method using a 24-hour recall
(Method B) for fruit, which had the highest criterion validity (see Chapter Four), could be
used to place people into appropriate stages based on the results from criterion validity
and construct validity. However, for vegetables, staging B (24 hour recall) showed
different patterns from those by Method A (self-rated intake) and staging Method B (food
frequency). Such ﬁndings mean that although stages for fruit by either perceived intake
(self-rated or food frequency) or a recall show similar patterns of psychometrics, stages
for vegetables only by self-rated and food frequency show similar patterns, but those by a
recall do not.

The strong relationships between self-efficacy and Stages of Change for fruit and
for vegetable intake in this study replicated the results shown in studies for smoking
cessation (De Vries et al., 1995). One study of self-efﬁcacy for eating low-fat intake
reported no difference in self-efficacy between the action and maintenance stages
(Oupuus et al., 1999). Data from this present study also showed that the scores for self-

' efﬁcacy between pre-action stages and post-action stages differed signiﬁcantly, but did
not differ within pre-action stages or within post-action stages- That is, the change in
self-efﬁcacy for ﬁ'uits and vegetables occurs between the three pre- and two po st-action

stages.

101

Signiﬁcant differences of pros and cons for eating low-fat were reported between
precontemplation and maintenance stages (Oupuus et al., 2000). Our data, however, did
not show such results for either ﬁ'uits or for vegetables, except for pros by Method B for
fruits and for cons by Method C for vegetables. From the results, pros tended to increase
and cons tended to decrease in advanced stages, which agrees with the results from other
behaviors (Prochaska et al., 1994). This result support the hypothese that people in
advanced stages consider pros more and cons less when they decide to eat fruits and
vegetables.

Comparison of the three staging methods by mean scores for the processes of
change among the stages of change showed similar relationships between stages and
psychometric scales by all three methods for eating fruits. However, for eating
vegetables, staging Method B (24-hour recall) did not show similar results to other
staging methods.

These ﬁndings need to be repeated with a larger number of people, because a small
number in certain stages can lead to loss of statistical power to detect differences where
they exist. Furthermore, longitudinal studies are necessary to ﬁnd the true changes in
pros, cons, self-efﬁcacy and use of processes for changing behaviors and to identify more
detailed information about people assigned to speciﬁc stages, including relapsers or
people in maintenance.

In Chapter Three, validity was established only based on a behavioral criterion.
However, a behavioral criterion is not the only factor to assign people into appropriate
stages, because Stages of Change has several dimensions to be determined (Prochaska

and DiClemente, 1984b). Therefore, in this chapter, other psychosocial factors, e. g.,

102

other dimensions of Stages of Change, were considered to establish construct validity
between stages and psychosocial factors by all three staging methods. From those
considerations, staging Method B (24 hour recall) for ﬁ'uit might be an appropriate
staging instrument satisfying both criterion and construct validity. For vegetables,
Method A (self-rated intake) and Method C (food frequency) had similar results for both
criterion and construct validity. Method B for vegetables does not seem to be an

appropriate staging method by any test of validity.

103

 

 

 

 

 

 

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108

Chapter Seven

CONCLUSIONS AND RECOMMENDATIONS

This chapter includes conclusions, strengths and limitations of this study.
Recommendations for future research are made based on those ﬁndings.

For college young adults, this study: 1) established outcome validity and reliability
for methods used to assign stage of readiness to eat adequate fruits and adequate
vegetables; 2) identiﬁed the processes of change for eating enough ﬁ'uits and vegetables;
and 3) reported some lifestyle factors associated with inadequate hit and vegetable
intakes.

Because use of self-rated intake to establish stage of change often failed to show
outcome validity for average fruit and average vegetable servings (Campbell et al., 1994;
Brug et al., 1997), other staging methods using different outcome assessments were
examined to establish outcome validity and reliability in this study. All three methods for
evaluation intake of fruits and vegetables--self-rated intake, a 24—hour recall and a food
ﬁ'equency (FFQ) for the past week--distinguished people in pre-action stages from those
in post action for ﬁ‘uits and vegetables as validated by the average fruit and vegetable
servings from a three-day food record. Using the criteria of at least 2 servings for fruits
and 3 servings for vegetables, the 24 hour recall method showed the highest agreement
and ability to detect individuals with inadequate ﬁ'uit intakes compared to the self-rated
intake and FFQ. The 24-hour recall method also showed good reliability (Cohen’s
K>0.4). Therefore, a 24-hour recall as the outcome assessment for staging readiness to

eat fruits was valid. For eating vegetables, ﬁirther studies are needed to improve staging

109

methods, because in this study all methods failed to show good agreement with vegetable
servings from a three-day food record. This ﬁnding was due in part to daily variation in
vegetable intake and vegetables disguised or overlooked in mixed dishes. In addition,
this ﬁnding is likely due to cognitive differences between consumers and nutrition
education researchers in regard to what is considered to be a vegetable. This is especially
true for fried potatoes. To assess people’s vegetable intakes more accurately, we need to
investigate how consumers conceptualize the vegetables they eat, e.g., cooked vegetables,
potatoes, side salads, condiments on sandwiches, etc.

The processes of change for eating enough fruits and vegetables were identiﬁed
and examined in relation to the stage of change of each subject. Compared to other
behaviors such as smoking cessation, fewer change processes for eating fruits or
vegetables were found. We speculate that this college population is less aware of the
importance of eating fruits and vegetables compared to that of other health behaviors.
Another reason for ﬁnding fewer processes in this study is that eating ﬁuits and
vegetables is a desirable behavior, not an avoidance behavior. The different uses of self-
reevaluation and health commitment/action processes by those in different stages suggest
that these processes'are important for young adults to change stages both for fruits and
for vegetables. Therefore, “feeling healthy” and “keeping fruits and vegetables around to
substitute for high fat foods” should be good strategies to promote adequate fruit and
vegetable intakes. However, health concerns were important only for vegetable intakes,
but not for fruit intakes.

In the original smoking cessation research on TTM-investigators examined

“relapsers” in a study on processes of change, but this group was not examined in the

110

dietary studies on processes (Bowen et al., 1994; Oupuus et al., 2000). The group
“maintenance by habit” used in this study was simply excluded in smoking cessation
studies as irrelevant (Prochaska et al., 1979). Findings from this study on processes of
change support that “relapsers” and “maintenance by habit” are important groups for
nutrition educators to identify, especially for eating vegetables. Self-reevaluation was
used more frequently by relapsers, and health commitment/action was less frequently
used compared to others in pre-action stages. Health concerns, self-reevaluation and
health commitment/action were less frequently used by peOple in maintenance by habit
compared to those in maintenance by change. These are important differences relevant to
nutrition educators for targeting their efforts.

For this study “inadequate” fruit and vegetable intakes were deﬁned as less than 2
servings of ﬁ'uits or fruits without juice and less than 3 servings of vegetables or
vegetables without ﬁied potatoes reported in a three-day food record. To help develop
and target effective interventions, lifestyle factors associated with “inadequate” fruit and
vegetable intakes can be identiﬁed. College young adults who ate “inadequate” amounts
of fruits were less likely to live in residence halls and to practice health behaviors such as
not smoking, regular exercising and eating breakfast. “Inadequate” ﬁuit intake was
positively associated with higher discretionary fat consumption, however, “inadequate”
vegetable intake was not related to any of these behaviors.

Strengths and Limitations

As expected, self-efﬁcacy was inversely associated with both inadequate fruit and

 

vegetable intakes. Different associations of pros and cons for mm and vegetable intakes

were found when ﬁ'uit juice and fried potatoes were excluded. The interpretation of

111

psychosocial factors in this study is limited because psychosocial factors were originally
measured for total fruit intake, including juice, and total vegetables, including ﬁied
potatoes. Therefore, to examine the differences of psychosocial factors for juice intake
and fried potatoes, separate questions would be necessary in the future and this is
recommended. When ﬁ'uit juice and fried potatoes were excluded, fruit and vegetable
intakes were positively associated with each other.

In this study, the data clearly showed different patterns for eating fruits than for
eating vegetables. Differences between eating fruits and eating vegetables include
several psychosocial and demographic factors, stage distribution, perceptions of eating
fruits and vegetables and the change processes used for eating enough fruits and
vegetables. Therefore, future nutrition education research must continue to separate
fruits and vegetables, even though the S-A-Day message for the public has combined the
two into one slogan. Because exclusion of ﬁuit juice and fried potatoes from total intakes
showed different results with psychosocial factors and intakes of other food groups,
future research should consider fruit juice and fried potatoes separately from total ﬁuits
and total vegetables when lifestyle factors associated with fruit intakes and vegetable
intakes are identiﬁed. ‘

A three-day food record was the “gold standard” to evaluate the outcome validity
of the staging methods for eating ﬁ'uits and vegetables and to examine factors associated
with inadequate fruit and vegetable intakes. However, because a longer period of intake
is desired for “usual intake” and because food intakes during seasons other than winter
might yield different results (Smith-Warner et al., 1997), a study using more days of

intakes over a longer period would be useful to conﬁrm our results.

112

A sample representative of young men not interested in nutrition would be
necessary to generalize these ﬁndings to all college students. Furthermore, other samples
ﬁ'om varied geographic regions are desirable, because results might differ somewhat in
each area. Finally, this study should be repeated with a larger number of people, because
the small number of people in certain stages, such as precontemplation and
contemplation, could have led to a loss of statistical power to detect differences where

they exist.

Recommenstions for ﬁxture studies

Based on the ﬁndings from this study, the following recommendations are made for

research on fruit and vegetable intakes.

1. This study was conducted with college students who were predominantly Caucasian
women, therefore, additional study is needed with different subpopulations at risk for
low intakes of fruits and vegetables, such as limited income and men.

2. Examples of vegetable consumption, not always perceived as vegetables, included
fried potatoes, side salads, spaghetti, pizza and vegetables on sandwiches. It is
necessary to develop appropriate assessment instruments to detect vegetable intakes
in the ways that people eat and conceptualize vegetables.

3. The association of psychosocial factors with intakes differed fro ﬁ'uits and fro
vegetables and differed when juices and ﬁied potatoes were excluded. Future studies
on dietary behaviors should continue to separate fruit and vegetable behaviors and

consider the sub-categories of fruit juice and fried potatoes.

113

4. This study established cross-sectional relationships between uses of processes of
change and stages of change for eating enough fruits and vegetables. However,
longitudinal studies are necessary to identify true changes in processes for changing .
behaviors. Longitudinal studies would also help identify detailed information about
people assigned to speciﬁc stages, including relapsers or people in maintenance,

while interventions are conducted.

114

APPENDICES

115

APPENDIX A

UCRIHS APPROVAL

116

MICHIGAN STATE
U N l V E R S l T Y
January 28, 1999

 

T0; Dr. Sharon L HOERR
204 GM Trout Building

APPROVAL DATE: January 28, 1999

RE: IRB# 98814 CATEGORY: l-C

TITLE: STAGE or READINESS To EAT DAILY FIVE OR MORE SERVINGS or
FRUITS AND VEGETABLES: VALIDITY, RELIABILITY AND PROCESSES
OF CHANGE " ‘

The University Committee on Research Involving Human Subjects' (UCRIHS) review of this
project is complete and I am pleased to advise that the rights and welfare of the human subjects
appear to be adequately protected and methods to obtain informed consent are appropriate.
Therefore, the UCRIHS approved this. project.

RENEWALS: UCRIHS approval is valid for one calendar year, beginning with the approval date
shown above. Projects continuing beyond one year must be renewed with the green renewal form.
A maximum of four such expedited renewals possible. Investigators wishing to continue a project
beyond that time need to submit it again for a complete review. - -
REVISIONS: UCRIHS must review any changes in procedures involving human subjects, prior to
initiation of the change. If this Is done at the time of renewal, please use the green renewal form.

To revise an approved protocol at any other time during the year, send your written request to the
UCRIHS Chair, requesting revised approval and referencing the 1310] ect's IRB# and title. Include

in your request a description of the change and any revised instruments, consent forms or
advertisements that are applicable.

PROBLEMS/CHANGES: Should either of the following arise during the course of the work, notify
UCRIHS promptly: 1) problems (unexpected side effects, complaints, etc.) involving human
subjects or 2) changes in the research environment or new information indicating greater risk to
the human subjects than existed when the protocol was previously reviewed and approved. '

If we can be of further assistance, please contact us at 517 355-2180 or via email.
UCRIHS@pilot. msu. edu. Please note that all UCRIHS forms and instruction are
located

“519.9 5; it)...

David E. Wright, Ph.D. é
UCRIHS Chair ‘

DEW: db

117

APPENDIX B

INFORMED CONSENT

118

Informed consent-Focus group interview, Step 1

Thank you for your willingness to participant in this study. You were selected to study
how we can increase intake of fruits and vegetables. Please plan to discuss honestly your
opinions about fruits and vegetables. '

Consent:

I understand that all data including audio tapes will be kept conﬁdential and reported in
group form only.

I volunteer to participate in this study to discuss factors aﬁ‘ecting my food intake.

I will participant in a small group discussion with the researchers.

I understand I will receive $10.00 in cash upon completion of the interview.

I can request information regarding the project at any time from Sharon L. Hoerr at 355-
77 01, Department of Food Science and Human Nutrition.

I am free to refuse this request without penalty.

I can refuse to answer particular questions.

I can withdraw at any time.

I am willing to participate in this study as indicated by my signature below.

Name(Print) :

 

(Signature): Date:

 

Phone:

 

E-mail:

 

Investigators: Sharon L. Hoerr, RD, PhD Professor, Department Food Science and
Human Nutrition at Michigan State University, College of Human Ecology, Phone
517/355-7701, hoerrs@pilot.msu.edu

 

I received $10.00 for my participation in What Young Adults Think About Fruits and
Vegetables.

 

 

Signature Social Security No. Date

119

How Young Adults Think about Fruits and Vegetables
Informed Consent —Step 2

Thank you for your willingness to participam in this study about how young adults think about
fruits and vegetables. By signing the consent from below, you indicate your permission for your
responses to be used for this study.

All data will be anonymous and reported in group form only. Your name will not be attached to
your responses.

You are free to refuse this request without penalty, reﬁise to answer particular questions or
withdraw at any time.

You will receive a coupon for a single ice cream cone at MSU Dairy Store upon cornpletion and
return of the questionnaire and for agreeing to participate in this study.

You can request information regarding the project at any time from Dr. Hoerr at 355-7701,
Department of Food Science and Human Nutrition.

Name (Print) : (Signature): Date:

 

We greatly appreciate your contribution. If you decide to participate, return this form outside the
classroom 30 minutes before class on Nov 18, 23 or 30. Ms. Mock will report results back to the
class in group form only. Thank you for your participation with this research project!

Investigator: Sharon L. Hoerr, RD, PhD, Professor, Department Food Science and Human
Nutrition at Michigan State University, College of Human Ecology, Phone 517/355-7701,

hoerrs@msu.edu

120

Food Habits of Young Adults
Step 3 - Baseline

Thank you for your willingness to participant in this study. Your class was selected to
help study fruit and vegetable intakes of young adults.

By signing below, you indicate your understanding that participation includes the
following, these things, as well as, your permission for your responses to be used for this
study.

1) Recording food intake for three days. A one day’s food intake includes all the foods
and beverages consumed during a 24 hr period.

2) Answering 5 pages of questions on demographics, fruits and vegetables.
3) Completing a short frequency of fruits and vegetables and a 24 hr food recall (3

pages).
You understand:
All data will be kept conﬁdential and reported in group form only.

You are free to refuse this request without penalty, reﬁrse to answer particular
questions or to withdraw at any time.

You will receive extra points in HNF 150 when you complete these instruments at
baseline, regardless of whether you permit us to use your responses for this study.

Name (Print) :

 

(Signature): Date:

You can request information regarding the project at any time from Dr. Sharon Hoerr at
355-7701, Department of Food Science and Human Nutrition, Michigan State University.

Investigators: Sharon L. Hoerr, RD, PhD Professor, Department Food Science and
Human Nutrition at Michigan State University, College of Human Ecology, Phone
517/355-7701, hoerrs@msu.edu; Sang-Jin Chung, Graduate student,
chungsa2@pilot.msu.edu

121

Food Habits of Young Adults
Step 3 - One-Two Week Later than Baseline

Thank you for your willingness to participant in this study. Your class was selected to
help study-ﬁ'uit and vegetable intakes of young adults.

By signing below, you indicate that participation includes completion of a short
frequency of fruits and vegetables and a 24 hr food recall. Your signature also indicates
your permission for your responses to be used in this study.

You understand:

All data will be kept conﬁdential and reported in group form only.

You are free to refuse this request without penalty, refuse to answer particular
questions or to withdraw at any time.

You will receive a coupon for single ice cream for repeating the short frequency of
ﬁuits and vegetables and a 24 hr food recall. The coupon is redeemable at the MSU
Dairy Store.

Name (Print) :

 

(Signature): Date:

 

You can request information regarding the project at any time from Dr. Sharon Hoerr at
355-7701, Department of Food Science and Human Nutrition, Michigan State University.

Investigators: Sharon L. Hoerr, RD, PhD Professor, Department Food Science and

Human Nutrition at Michigan State University, College of Human Ecology, Phone
517/355- 7701, hoerrs@msu. edu; Sang-Jin Chung, Graduate student,
chungsa2@pilot. msu. edu

122

APPENDIX C
FOCUS GROUP INTERVIEW PROTOCAL & INSTRUMENT (STEP 1)

123

Processes of Change Focus Group Interview Protocol — for interviewers
8/20/99-9/20/99 Conduct interview

Interview complete chart

# Female-Col 18-24 Male-Col 18-24

 

I. Interviewer Training

Training will include:
1) describe the purpose of the study
2) review the manual, script, and questionnaire
3) practice interviews

Objectives of the interview are to determine

-,what processes subjects use to eat fruit and to eat vegetables-,
and - questions or clariﬁcation needed on items

will ask subjects to review the list of processes (attached) to see which one apply to
themselves and to get their additional comments.

Interviewer

- should be someone close in age to the research participants.

- should be educated about the processes of change —,to be able to respond when
subjects have questions about them.

- will get information on strategies of how to increase fruit and vegetable intake during
the interviews

- learn how to facilitate interview effectively.

124

II. Conducting the Interview

Supplies need: ,

- Tape player — tapes and extra batteries, extension cord(s)
- C0py of interview questions

- Consent forms

A. Interview Format and Questions

Greet each participants at the door with a smile.

2. Start the interview by making the participant feel comfortable, introduce yourself and
thank the participant for his/her time.

3. Have the participant sign the consent form.

4. Ask participant, if the interview can be audio taped. Tell him/her it will only be used

for research purposes, only ﬁrst names will be used and all data will be destroyed

after the data are merged into a group data ﬁle.

Indicate the interview will, take between 1 - 11/2 hour to complete.

Start recording the session.

7. Tell the participant the purpose of the interview.

“ My purpose is not to evaluate your diet, so don’t be afraid to be honest. I would like

to understand one particular aspect of what you eat, so I will be asking you some

questions only about the ﬁ'uits/or vegetables you eat. First, we will start with your

intentions for eating fruits/vegetables. Have you tried to eat adequate amounts of

fruit/vegetable or to increase ﬁ'uit/vegetable intake?”

)—

9}"

B. Interview Questions

“ - Think about what you or your friends have done and thought to eat adequate amounts

of ﬁ'uit/vegetable, to increase fruit/vegetable intake or to eat fruit/vegetable.

- Please write these actions and thought on the cards given. I’ll collect the cards at the
end of session. (5 min)

Discuss. Then, interviewer gives the participant a draft of questionnaires about processes

of change and says,

- Read through this list of processes and instructions. These are items already
developed by experts. For each section, answer the question. I will go through each sub-
category with you. ,

- Start with consciousness raising part, check which processes apply to your
fruit/vegetable intake. You can choose never to always. Do they make sense? Is wording
clear? If not, please let us know. ‘

- After that, think about whether you or your friends use different processes than those
listed here. You can go back the items you wrote down at the beginning of this session. If
there are, please write these down.”

125

Interviewers check items in each category, “A. Consciousness raising” to “L. Helping

relationship”.
Then, ask an ending question. “Of all the things we discussed, what to you is the most

important?”

III. Closing the interview

After ﬁnishing all items in the 11 categories, collect participants’ questionnaires.
When interviewers collect questionnaires and consent form (It is very important to get
their signature and social security number), give $10.00 to each participant as an
incentive.

126

Process of Change Focus Group Interview Instrument

Part V. This last part assesses ways that you think about fruits/vegetables and things that you
and others might do relative to fruits/vegetables. There are 11 subcategorizes.1

"Adequate amounts mean fruit intake more than or equal to 2 servings and vegetable
intake more than or equal to 3 servings'

 

Please Indicate how frequently you currently do the following, when you eat Fruits and
Vegetables. Circle the best response from:
1=Never 2=Seldom staccasionally
4=Often 5=Always

 

 

A. Consciousness Raising, e. g., ‘I have been increasing Never Always
information about myself and the fruits and vegetable I eat.”

 

1. I think about why it is good for me to eat adequate amounts
of

 

N
w
A
01

Fruits 1
Vegetables 1 2 3 4 5

 

2. Others have made me think about the health benefits of
eating adequate amounts of
Fruits
Vegetables 1 2 3 4 5

N
w
A
0'!

 

9"

I think about information regarding my health problems from
not eating adequate amounts of

 

{Fmits 1 2 3 4 5
Vegetables 1 2 3 4 5
4. I pay attention to how my eating adequate amounts of
helps prevent disease and constipation.
Fruits 2 3 4 5
Vegetables 2 3 4 5

 

5. | read and/or listen to information about the I eat.

 

 

Fruits 1 2 3 4 5
Vegetables 1 2 3 4 5
6. Ipay attention to ways I can eat adequate amounts of
Fruits 2 3 4 5
Vegetables 2 3 4 5

 

7. I have been evaluating my feelings regarding the I
eat. Fruits 1 2 3 4 5
Vegetables

 

 

 

 

 

 

 

 

 

 

127

 

I am willing to listen to advice to eat adequate amounts of

 

 

 

 

 

 

 

 

Fruits 2 3 4
Vegetables 2 3 4

I realize that adding to meals provides a variety of
ﬂavors. Fruits 2 3 4
Vegetables 2 3 4

 

128

 

 

 

B. Dramatic Relief, e.g., ‘I have been using feelings to Never Always
motivate me to eat enough fruits and vegetables. "

 

1. Warnings about the health problems of eating inadequate
amounts of cause me concern.
Fmits
Vegetables 1 2 3 4 5

N
(a)
.h.
0"!

 

2. A family history of chronic disease has caused me to
consider the amount of I eat.
Fruits
Vegetables 1 2 3 4 5

N
00
A
01

 

3. I react emotionally to stories about health problems of
people who did not eat adequate amounts of

 

Fruits 1 2 3 4 5
Vegetables 1 2 3 4 5
4. Watching a loved one die who had poor dietary habits. has
made me think about the I eat.
Fruits 1 2 3 4 5

Vegetables 1 2 '3 4 5

 

5. I eat adequate amounts of so I do not get
constipated. Fruits 1 2 3 4 5
Vegetables 1 2 3 4 5

 

6. I am happy my family members stay healthy by eating
adequate amounts of . Fruits
Vegetables 1 2 3 4 5

N
0)
.h
01

 

7. I feel great because eating adequate amounts of
helps maintain or lose my weight.

Fruits 1

Vegetables 1 2 3 4 5

N
00
.5
01

 

8. I've seen the consequences of a diet limited in

 

Fruits
Vegetables 1 2 3 4 5

N
(a)
.5
U!

 

 

9. I think about how good my skin looks when I eat adequate

amounts of
Fruits 1
Vegetables 1

NM
0900
h-h
U'IUI

 

 

 

 

 

 

129

 

 

 

 

 

 

 

 

 

 

 

 

 

C. Self Re-evaluation, e.g., ' I have been assessing howl feel Never Always
and think about how many fruits and vegetables I eat. "
1 2 3 4 5
1. I take it as personal challenge to ﬁnd ways to eat adequate
amounts of
Fruits 1 2‘ 3 4 5
Vegetables 1 2 3 4 5
2. I feel healthy and vitalized when I eat adequate amounts of
each day.
Fruits 1 2 3 4 5
Vegetables 1 2 3 4 5
3. I see eating adequate amounts of as part of my role
to be a responsible and healthy person.
Fruits 2 3 4 5
, Vegetables 2 3 4 5
4. I feel good about myself when I eat adequate amounts of
Fruits 2 3 4 5
Vegetables 2 3 4 5
5. I feel frustrated when conforming to others' food preferences
keeps me from using in my menu plans.
Fruits 1 2 3 4 5
Vegetables 1 2 3 4 5
6. I have recently evaluated whether I eat adequate amounts
of . Fruits 2 3 4 5
Vegetables 2 3 4 5
7. I review what I have eaten over a few days. and that helps
me eat adequate amounts of
Fruits 1 2 3 4 5
Vegetables 1 2 3 4 5
8. I think are taste good.
Fruits 1 2 3 4 5
Vegetables 1 2 3 4 5

 

 

 

 

 

 

130

 

 

D. Environmental Re-evaluation, e. g., ‘ 1 consider the Never Always
influence of the fruits and vegetables I eat on the environment. ”

 

1. I think about how my organization could benefit health-wise ,
if members would eat adequate amounts of

 

 

Fruits 2 3 4 5
Vegetables 1 2 3 4 5
2. I think about health beneﬁts to society, if everyone would eat
adequate amounts of
Fruits 2 3 4 5
Vegetables 1 2 3 4 5
3. I think about how everyone needs to understand the
beneﬁts to the environment of eating adequate amounts of
Fruits 1 2 3 4 5

Vegetables 1 2 3 4 5

 

4. I think about how many people would beneﬁt health-wise
from having adequate amounts of

N
(A?
.p.
01

Fruits
Vegetables 1 2 3 4 5

 

 

 

 

 

5. I think about how eating is better for the environment
than eating meat.
Fruits 1 2 3 4 5
Vegetables 1 2 3 4 5
6. I'm concerned about environment (and how we treat
animals), therefore I want to eat more
Fruits 2 3 4 5
Vegetables 2 3 4 5
7. I'm concerned about pesticide residues, and it inﬂuences
the I eat.
Fruits 2 3 4 5
Vegetables 1 2 3 4 5
8. My eating adequate amounts of affects those with
whom I live.

N
O)
.h
0'!

Fruits 1
Vegetables 1 2 3

 

 

 

 

 

A
U"

 

131

 

 

 

I feel uncomfortable when I think about being somewhere

that won't have for me to eat.

Fruits
Vegetables

 

 

MN

 

OD

 

#«h

 

132

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

E. Social Liberation, e.g., ‘ Society in general, and the places I Never Always
go support eating adequate amounts of trait and vegetable. " 1 2 3 4 5
1. When I go out, I can ﬁnd choices for snacks.
Fruits 1 2 . 3 4 5
Vegetable 1 2 3 4 5
2. I notice many choices for when eating out.
Vegetable 2 3 4 5
3. Where I shop there is a good selection of
Vegetables 1 2 3 4 5
4. Others have made a dietary change which has inﬂuenced the
| eat.
Fruits 2 3 4 5
Vegetables 2 3 4 5
5. I ﬁnd society supportive of pe0ple eating adequate amounts
of . Fruits 2 3 4 5
Vegetable 2 3 4 5
6. Many people I know are eating adequate amounts of
Fruits 1 2 3 4 5
Vegetable 1 2 3 4 5
7. I see eating adequate amounts of being promoted in
my community.
Fruits 1 2 3 4 5
Vegetable 1 2 3 4 5
8. I notice ' 5 A Day’ signs promoting eating adequate amounts
of _. Fruits 1 2 3 4 5
vegetable 1 2 3 4 5
9. I ﬁnd people want that they like to eat adequate
amounts of
Fruits 2 3 4 5
Vegetable 2 3 4 5
10. I ﬁnd people like at each meal, because they are
typically served/available at dinner, lunch, breakfast or snack.
Fruits 1 2 3 4
Vegetables 1 2 3 4 5

 

 

 

 

 

 

133

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

G. Self Liberation, e.g., ' I recognize food choices and have ”ever “ways
made a commitment to eat adequate amounts of fruits and 1 2
vegetables." 4 5
1. I look for ways to include in recipes and mixed
dishes and on sandwiches.
Fruits 1 2 4 5
Vegetable 1 2 4 5
2. I buy to help me follow a good diet.
Fruits 2 4 5
Vegetables 2 4 5
3. Choosing adequate amounts of gives me a feeling of
control. Fruits 2 4 5
Vegetables 2 4 5
4. I plan ahead to eat adequate amounts of
Fruits 2 4 5
Vegetables 2 4 5
5. I make commitments to eat adequate amounts of
Fruits 2 4 5
Vegetables 1 2 4 5
6. I imagine nutrients in the I eat ﬁghting disease in my
body. Fruits 1 2 4 5
Vegetables 1 2 4 5
7. I know eating adequate amounts of is easy. 1 2
Fruits 1 2 2 5
Vegetables 5
8. I made a New Year’s resolution to eat adequate amounts of
. 2 4 5
Frurts 1 2 1 5
Vegetables
9. I have told others that I want to eat adequate amounts of
. 2 4 5
Frurts 2 1 5
Vegetables
10. I know that I can eat adequate amounts of 2 1 5
Vegetables
11. l have set a goal (or am working on a plan) to eat adequate
amounts of . Fruits 1 2 4 5
Vegetables 1 2 4 5

 

 

 

 

 

 

134

 

 

 

 

 

 

 

 

 

 

 

H. Counter-conditioning, e. g., " I made substitutions that help Never Always
me eat fruits and vegetable. "
1 2 3 4 5
1. Instead of eating unhealthy foods, I eat
Fruit 2 3 4 5
Vegetables 2 3 4 5
2. I ﬁnd that ordering is good substitute for other foods
in restaurants.
Fruit 1 2 3 4 5
Vegetables 2 3 4 5
3. I eat as snacks, when I have a craving for a high calorie
food.
Fruit 1 2 3 4 5
Vegetables 1 2 3 4 5
4. l substitute , when I want to eat high fat foods.
Vegetables 2 3 4 5
5. Eating adequate amounts of satisﬁes me physically,
instead of eating other foods.
Fruit 2 3 4 5
Vegetables 2 3 4 5
6. When I crave a food, I think about eating instead.
Fruit 1 2 3 4 5
Vegetables 1 2 3 4 5
7. I am decreasing my fat, so I am purposely eating adequate
amounts of _
Fruit 1 2 3 4 5
Vegetables 1 2 3 4 5

 

 

 

 

 

135

 

 

 

 

 

 

 

 

 

 

I. Stimulus control, e.g., ' I have changed my environment to Never Always
encourage eating adequate amounts of fruits and vegetables. ”
1 2 3 4 5
1. I try to keep around my place, in case I feel like
eating something.
Fruit 2 3 4 5
Vegetables 1 2 3 4 5
2. I eat breakfast, which helps eat adequate amounts of
Fmit 1 2 3 4 5
Vegetables 1 2 3 4 5
3. I keep reminders to eat adequate amounts of
Fruit 1 2 3 4 5
Vegetables 1 2 3 4 5
4. I remove foods around me that I used to choose instead of
Fruit 2 3 4 5
Vegetables 2 3 4 5
5. I keep on hand as lunch and/or snack, when I’m on-
the-run. Fruit 1 2 3 4 5
Vegetables 1 2 3 4 5
6. There are visible in my house.
Vegetables 1 ' 2 3 4 5

 

 

 

 

 

 

136

 

 

 

 

 

 

 

 

J. Interpersonal control, e. g., ' I have avoided other people Never Always
who act as barriers to eat fruits and vegetables. "
1 2 3 5
1. I leave places where people are eating high fat foods
instead of eating
Fruit 2 3 5
Vegetables 1 2 3 5
2. I change personal relationships which contribute to my
inadequate
Fruit 2 3 5
Vegetables 2 3 5
3. I relate less often to people who contribute to my inadequate
Fruit 2 3 5
Vegetables 2 3 5

 

 

 

 

 

 

137

 

 

 

 

 

 

 

 

 

K Contingency management e. g., " I reward myself or am Never Always
rewarded for eating adequate amounts of traits and vegetables. "
1 2 3 4 5
1. I praise myself when I eat .
Fruit 1 2 -3 4 ' 5
Vegetables 1 2 3 4 5
2. I expect to be rewarded by others when I eat
Fruit 1 2 3 4 5
Vegetables 1 2 3 4 5
3. I feel better (or my clothes ﬁt better) since I have eaten
adequate amounts of
Fruit 2 3 4 5
Vegetables 2 3 4 5
4. I do something nice for myself, when | eat enough
Fruit 2 3 4 5
Vegetables 2 3 4 ,5
5. When I eat adequate amounts of , I believe I am doing
something nice for myself.
Fruit 1 2 3 4 5
Vegetables 1 2 3 4 5

 

 

 

 

 

 

138

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

L Helping relationship, e.g., ' I have someone I can be open Never Always
with regarding the fruits and vegetable I eat. "
1 2 3 4 5
1. I discuss with my friends the beneﬁts to health of eating
adequate amounts of
Fruit 1 2 3 4 5
Vegetables 1 2 3 4 5
2. My parents or caregivers care about eating adequate
amounts of , because they know the beneﬁts for
reducing chronic disease.
Fruit 1 2 3 4 5
Vegetables 1 2 3 4 5
4. Others encourage me to eat adequate amounts of
Vegetables 1 2 3 4 5
5. I count on others to support my dietary changes for eating
adequate amounts of
Fruits 1 2 3 4 5
Vegetables 1 2 3 4 5
6. My friends offer with every meal.
. Fruit 1 2 3 4 5
Vegetables 1 2 3 4 5
7. I discuss with friends, the health beneﬁts of eating adequate
amounts of
Fruit 1 2 3 4 5
Vegetables 1 2 3 4 5
8. Others in my life are also making dietary changes that have
made it easier for me to eat adequate amounts of
Fruit 2 3 4 5
Vegetables 2 3 4 5
9. Friends/family admire me when I eat adequate amounts of
Fruit 1 2 3 4 5
Vegetables 1 2 3 4 5
10. Other people in my daily life try to make me feel good when I
eat adequate amounts of
Fruit 2 3 4 5
Vegetables 1 2 3 4 5

 

 

 

 

 

 

139

 

 

11. l have someone who cares whether I get adequate amounts
of

 

 

 

 

 

 

 

 

 

 

Fruit 2 3 4 5
Vegetables 2 3 4 5
12. l associate with people who help me eat adequate amounts
of
Fruit 2 3 4 5
Vegetables 2 3 4 5
13. Special people in my life accept me, whether or not I eat
adequate amounts of
Fruit 2 3 4 5
Vegetables 2 3 4 5

 

140

 

APPENDIX D

QUESTIONNAIRES FOR REFINING ITEMS OF PROCESSES OF CHANGE
(STEP 2)

141

 

 

 

 

 

 

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149

APPENDIX E

STAGING INSTRUMENT A (SELF-RATED F/V INTAKE), B (24HR RECALL) &
C (F/V FOOD FREQUENCY), DEMOGRAPHICS, FINAL PROCESS OF '
CHANGE, DECISIONAL BALANCE, SELF-EFFICACY & 3 DAY FOOD

RECORDS (STEP 3)

150

 

 

 

 

 

 

 

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151

 

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156

Next, we will get the information about your food intake during 24 hour period.
Write down what you ate yesterday, including the time of day, the food you consumed

and how much of each food you ate. Start with breakfast...

Please remember to include all ingredients for each food, so that the data can be analyzed

with few errors. Below is a list of tips for ﬁlling out the forms easily and accurately.

The attached chart will assist you with you with accurately writing the foods you

ate.

- Breakdown recipes into speciﬁc foods or breakdown the food into its
components. For example, a peanut butter and jelly sandwich must be broken
into certain amounts of peanut butter, jelly and bread. Do the same for salads
and casseroles.

- Don’t forget snacks you had for a break, on the way to or from work, classes etc.

- Specify the type of food you ate. For example, if you had bread, what was the brand
name and was it wheat or rye? If it was wheatbread, was it reﬁned, whole wheat or
cracked wheat?

- Accurate portion size is important for your dietary analysis. Please indicate how
much you had of each food using stande measures—ounces, cups,teaspoons,
tablespoons, slice etc.

- And last, don’t forget condiments. Include on the form foods such as mayonnaise,

catsup, sugar, creamer and salad dressings.

157

 

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160

24 HOUR FOOD RECALL

Day of week

Time Food Item ' 'on and 'on Amount

       

1. Did you take a vitamin, mineral, herbal or other supplement today? Yes _ No—
If yes, Name and Brand and amount.

 

2. Was this your usual intake for fruits and vegetables? Yes
No

Ifnot, what and how much more or less do you usually eat?

 

 

3. How long have you eaten this way?
3. Less than 6 months
b. More than 6 months

161

 

Pa

Part II. Demographics

99°.“

10.

11
12

l3.

14

15
16
17
18
19

20.

. Current height? 11 in

Current weight? lb

How satisﬁed are you with your current weight? _very satisﬁed _satisﬁed _neutral
_unsatisﬁed _very unsatisﬁed

. How old are you? years

Do you live in a _residence hall _apartment or house?

What is your gender? male female. ~
If female, are you pregnant? _No _Yes; are you breast feeding? _No _Yes

What is your major?
Total number in your household or living space including yourself _.
Are you living with parents? _No _Yes

Are you living with ﬁiends? No Yes

 

Are you married/living with a partner? _No _Yes.

Are there any children in your household? _No _Yes. Ifyes, how many children _?
Do you currently have jobs or volunteer activities? No . Yes

 

If yes, How many hours are you involved in them per week?

What is your race or ethnic group?

White (not of Hispanic origin) _ American Indian/Alaska Native
Black (not of Hispanic origin) __ Asian/Paciﬁc Islander
Hispanic/Latino . __ Other

Times a week you eat breakfast?

Times a week do you eat a meal or snack at a fast food restaurant? _
Do you smoke cigarettes? _No _Yes. Ifyes, how many a day _?
Do you exercise regularly? _No _Yes, If yes, How many hours a week _?
Do you drink alcoholic beverages? _No éYes.
If yes, how many drinks per week _ (one drink = 12 oz beer, 5 oz wine, 1 shot liquor)?

Do you take vitamin or mineral supplements? No Yes, If yes, what kind (brand

name or which nutrient)

and how much ?

Questions about fruit and vegetable

For Fruit

1.
2.

3.

Is you§ current amount of fruit intake similar to intake you had in your childhood? No
es,

Have 1you tried t3? increase your fruit intake? No Yes, Ifyes, has it been successful?
_ 0 .'_ es

Have you participated in any intervention program to promote increasing fruit intake?
No Yes

162

4. How many servings of fruit intake is enough to maintain your good health?
(Onepiece offnnt, 1/2cupoffruit,’/4cupof100%fruitjuioersaserving.)

For Vegetable
1. Is yourycurrent amount of vegetable intake similar to intake you had in your childhood? No
85,

2. Iiiwe you gied to increase your vegetable intake? No Yes, Ifyes, has it been successful?
__ o _ es

3. liave you participated in any intervention program to promote increasing vegetable intake? No
es

4. How many servi s of ve etable intake is enough to maintain your good health?
(1/2 cup of cook veg 1e, 1 cup of leafy vegetable is a serving.)

163

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Part VII. 3 day Food Records - Finally, we would like to get your three-day food
records (three 24 hour periods). Please read instruction below carefully all foods
eat.

Instruction for 3-day food record

< For two consecutive weekdays (excluding Friday) and either Saturday or Sunday,
record everything YOU eat and drink, excluding water. Record the food you eat on
the Food Record Analysis Forms. Use one form for each day.

< Record, for each day, only the amount of each food you ACTUALLY EAT -This will
not necessarily be the same amount that was on your plate.

< Keep each day's food record by clock time of day, starting with the ﬁrst thing you eat
or drink after 5:00 AM and continuing until 5:00 AM the next day. If you eat or drink
during the night,- record this on the previous day‘s record, not on the next day's.
Group the items you eat and/or drink together next to a single clock time. (Time -
This means clock time, for example 7:00 AM or 3:30 PM.)

< The attached chart in page 5-6 will also assist you with accurately recording the foods
your have eaten.

(Chart attached next to a 24 hour recall form)

170

24 HOUR FOOD RECORD

Day of 3

Subject Number
Day of Week
Date

 

Time Food item Description Amount
And 'on

 

Did you take a vitamin or mineral supplement today? Yes No

If yes, Name and Brand and Number

 

I71

24 HOUR FOOD RECORD

Day of 3

Subject Number
Day of Week
Date

 

Time Food item Description Amount
And 'on

 

Did you take a vitamin or mineral supplement today? Yes No

Ifyes, Name and Brand and Number

 

172

24 HOUR FOOD RECORD

Day of 3

Subject Number
Day of Week
Date

 

Time Food item Description Amount
And 'on

 

Did you take a vitamin or mineral supplement today? Yes No

Ifyes, Name and Brand and Number

 

173

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174

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