The influence of the healthy eating for life program on eating behaviors of nonmetropolitan congregate meal participants

The influence of the healthy eating for life program on eating behaviors of nonmetropolitan congregate meal participants

Cynthia A. Long

The older adult population in the United States is growing quickly (Price, 2001). The older adult population is projected to increase throughout the next several decades. In 2000, for example, 35.0 million Americans (12.4 percent) were 65 years old and older (Hetzel & Smith, 2001). By 2010, 39.7 million Americans (13.2 percent) will be 65 years old and over, and by 2030, up to 20 percent of the U.S. population will be over age 65 (U.S. Census Bureau, 2000a; U.S. Census Bureau, 2000b). Along with this redistribution of the U.S. population, concerns related to aging may increase, including those related to the health and well-being of the older generation (Rogers, 1999).

For example, the U.S. Department of Agriculture reported that Americans” diets need to improve, including those of the elderly (Basiotis, Carlson, Gerrior, Juan, & Lino, 2002). Although aging is not itself a cause of malnutrition, related risk factors can affect older adults’ nutritional intake, contribute to malnutrition (Wellman, Weddle, Kranz, & Brain, 1997), and be “multiple and synergistic” (American Dietetic Association [ADA], 2000). Other factors that may contribute to the dietary status of the members of this growing older population are the types of nutrition messages they receive and their readiness to change diet-related behaviors.


A 1996 report by the American Dietetic Association discussed the increased challenges of competing with conflicting nutrition messages that consumers receive from a variety of sources. The public needs science-based information that not only educates, but also encourages the adoption of more healthful nutrition-related behaviors. An update of this Association’s report notes that research is needed to develop and test cost-effective methods for evaluating the efficacy of nutrition education programs. For effective behavior change, nutrition education programs must be based on the target audience’s needs, behaviors, motivations, and desires. And the gap between knowledge of nutrition and actual healthful eating practices must be narrowed by providing nutrition information in a usable form to consumers (ADA, 1996).

In the 1970s, Prochaska and colleagues began studying how people make changes. Their efforts led to the development of the Transtheoretical Model, of which the Stages of Change is a construct (Prochaska, Norcross, & DiClemente, 1994). Prochaska, attempting to bring together the components of the major psychotherapy theories regarding how people acquire successful behavior change, found that the many theories could be summarized by principles called the “processes of change.” He was especially interested in how “self-changers” progress along a continuum of change–from Precontemplation to Contemplation, Preparation, Action, Maintenance, and Termination–without therapy or a professional program (box 1).

Box 1–Basic definitions of the Stages of Change Construct of the

Transtheoretical Model and operational definitions used in this study

Basic definition Operational definition


No intention of changing Participant consumed fewer than 3 to

behavior and does not see a need 4 servings of fruits (vegetables)

to change. each day and did not say he or size

was seriously thinking about eating

more servings of fiuits (vegetables)

during the next 6 months.


Acknowledges need to change Participant consumed,fewer than 3 to

behavior and begins to think 4 servings offuits (vegetables)

seriously about doing so during each clay and said he or she was

the next 6 months or so. seriously thinking about eating more

servings offruits (vegetables)

during the next 6 months.


Plans to take action during the Participant consumed fewer than 3 to

next month to change a behavior. 4 servings of fiuits (vegetables)

each day and was planning to eat

more servings offuits (vegetables)

during the next 30 days.


Takes action to change behavior Participant consumed 3 to 4 or more

but action has lasted for 6 servings offuits (vegetables) each

months or less. day and has been consuming this

amount of fruits (vegetables) for

6 months or less.


Has been practicing a changed Participant consumed 3 to 4 or more

behavior for more than 6 months. servings of fruits (vegetables)

each day and has been consuming this

amount of fruits (vegetables) for

more than 6 months.


Has reached ultimate goal of

behavior change, with no concern

for relapse.

Note: Stages of change definitions are by Prochaska,

Norcross, and DiClemente (1994).

According to this construct, successful change requires that self-changers know the stage in which they are located and subsequently use appropriately timed strategies. Initial thoughts were that self-changers moved linearly from one stage to the next. In reality, successful self-changers may recycle through the Stages of Change several times before successfully reaching the Maintenance or Termination stage (Prochaska, Norcross, & DiClemente, 1994).

In studies of health behaviors, older adults have been found to fall primarily into the Precontemplation or Maintenance stage, therefore, calling for nutrition education efforts to be targeted at the Precontemplation stage (Nigg et al., 1999), where people do not perceive there is a need to change. The assumption is that people at the Precontemplation stage for adoption of a healthful diet need information that assists them in becoming aware of the personal benefits of healthful eating behaviors (Laforge, Greene, & Prochaska, 1994). Persons in the Maintenance stage–where behavior changes have occurred for more than 6 months–may experience some relapse (Kristal, Glanz, Curry, & Patterson, 1999), may need infor-mation about local resources, and may need strategies to help them deal with barriers to maintaining their dietary changes.

Implications for nutrition education programs for older adults include understanding and applying successful program elements, providing a clear plan for education and having that education based on segmented needs of the older population, adapting locally, and using existing services to provide education. These implications point to the need for research of behavior-based nutrition education for older adults (Contento et al., 1995). Thus, this study examines the influence of a nutrition education intervention–the Healthy Eating for Life Program (HELP)–on the eating behaviors of a select group of older adults that participated in congregate meal programs. Because the scientific evidence supporting the healthful benefits of fruit and vegetable consumption is significant (U.S. Department of Health and Human Services [DHHS], 2000; Tate& Patrick, 2000; Gerrior, 1999), we focus on behavior changes related to the consumption of these food items.

According to current research, older adults may maintain or improve their health by increasing their intake of fruits and vegetables, thus possibly lowering health care costs and increasing their quality of life (ADA, 2000; Gerrior, 1999). Nutrition education curricula for older adults are available for use, but the ability of these curricula to increase the servings of fruits and vegetables consumed by older adults is uncertain (Clarke & Mahoney, 1996; Contento et al, 1995). Hence, more evaluation studies are needed of the influence of nutrition education programs that are designed for older adults at congregate meal sites.



The target population for this study consisted of community-dwelling, nonmetropolitan older adults who attended congregate meal sites. The participants were at least 60 years old (as required for attendance at the congregate meals), with the exception of spouses under 60 years old who could attend meals when accompanying their older spouse.

The treatment group was chosen from three Ohio counties; the control group, from another Ohio county. (1) The Area Agency on Aging, county offices of Ohio State University Extension, and coordinators of the congregate meal sites assisted with site selection, which needed to be more rural than urban or nonmetropolitan. (2) Fifty treatment and 51 control participants were selected. (3)

Survey Instruments

Three instruments were used in this study: a demographics instrument, a questionnaire entitled Checkup on Your Good Eating Practices, and a Stages of Change instrument that consisted of two subscales–one for fruits and another for vegetables. These instruments were developed by Extension nutrition professionals of the HELP Elderly Nutrition Education Coordinating Group that developed the HELP instructor’s manual.

The demographics instrument collected information on gender, age, race, number in household, educational level, income, how often meals were eaten with someone else, and how often meals and snacks were eaten. Checkup on Your Good Eating Practices consisted of seven questions related to eating fruits and vegetables, and the Stages of Change instrument consisted of eight separate questions, four each for fruits and for vegetables (box 2). Questions on the Stages of Change instrument asked older adults the number of servings of fruits and vegetables they were eating, how long they had been eating that number of servings, and whether they were seriously thinking of increasing this number either in the next 30 days or in the next 6 months. These questions were based on the criteria of the Transtheoretical Model Stages of Change construct (W.D. Hart, personal communication, October 19,2001). Thus, the questions were based on a standardized length of time individuals had been working on, or intended to implement, a behavior change.

Box 2–Major Survey Instruments (1)

Checkup on Your Good Eating Practices: Example questions

(Answer choices: Almost never, Seldom,

Often, Almost always, and Doesn’t apply.)

What do you do?

Include at least three food groups in my breakfast

(e.g., milk, fruit, and grains such as bread and cereal)?

Eat 3 or more servings of different vegetables daily?

Eat at least 1 serving of vitamin A-rich foods daily

(e.g., dark green, leafy [spinach, kale, broccoli] and deep yellow

[sweet potatoes, cantaloupe, apricots])?

Choose potatoes prepared in lower fat ways (not fried)?

Eat 2 or more servings of different fruits daily?

Choose at least 1 serving of vitamin C-rich foods daily

(e.g., orange juice, grapefruit, broccoli, cabbage, tomatoes)?

Include at least 1 serving from each of the five food groups

(i.e., grains, fruits, vegetables, meat group, and milk products)?

Stages of Change: Questions

Separate questions were asked for fruit- and vegetable-eating


How many servings of fruits (vegetables) do you eat each day?

1 or 2

3 or 4

5 or more

Don’t know

About how long have you been eating this amount of fruits


Less than 1 month

1 to 3 months

4 to 6 months

Longer than 6 months

Don’t know

Are you seriously thinking about eating more servings of

fruits (vegetables) starting sometime in the next 6 months?



I already eat enough


Are you planning to eat more servings of fruits (vegetables)

during the next 30 days?



I already eat enough


(1) HELP evaluation instruments developed by Mary P. Clarke,

PhD, RD: Jacquelyn McClelland, PhD, RD; William D. Hart,

PhD, RD; and Alma Montano Saddam, PhD, RD of the Elderly

Nutrition Education Coordinating Group.

The Extension nutrition specialists, dietetic nutrition professionals, and county Extension agents (who also field tested the teaching materials) tested the instruments for content and face validity. The instruments were reviewed for content accuracy and suitability for the older adult target audience, after which appropriate adjustments were made.

Extensive field testing addressed any issues related to reliability. Cronbach’s Alpha was used to test internal consistency of the instruments. The instrument Checkup on Your Good Eating Practices tested at an alpha of .77. The subscale for Stages of Change for fruit-related behaviors tested at an alpha of .53, and the subscale for Stages of Change for vegetable-related behaviors tested at an alpha of .63. Research in applying the Stages of Change construct to measurement of behavior change of nutritional behaviors is relatively new. Therefore, the alpha levels were considered acceptable (Nunnally, 1967).

Treatment and Analysis

The HELP was developed as a joint project of the Cooperative Extension Services at Kansas State University, The Ohio State University, North Carolina State University, and St. Louis University. The program’s theme focused on having participants depend primarily on food for good nutritional health and encouraging them to eat a variety of nutritious foods even though the adults’ calorie needs may have declined. HELP lessons were designed to facilitate movement of nutrition behaviors along a continuum–from being unaware of eating habits and health connections to applying skills to maintain healthful eating behaviors (Clarke & Mahoney, 1996).

The HELP lessons specifically addressed nutritional needs of older adults. The connection between good health and healthful eating habits was emphasized. The fruit and vegetable lessons also presented practical ways for small households to purchase and store fruits and vegetables. Suggestions were shared for preparing fruits and vegetables that are easier to chew; lower in salt, sugar, and fat; and preserve other nutrients. The recipes, varying in texture, flavor, and temperature, were chosen because of their ability to appeal to the changing taste buds of many older adults.

The treatment group was taught a series of four HELP nutrition lessons. The lessons for the first 2 weeks focused on vegetables, with a lesson on potatoes included, while the second 2 weeks focused on fruits. The objectives of the lessons related to the following: suggested number and sizes of servings; vegetables and fruits as sources of various nutrients and few calories; links between eating vegetables and fruits and decreased risk for some diseases; cost-effective purchasing, storage, and preparation of vegetables and fruits; and vegetables and fruits with less fat, salt, and sugar.

A dish featuring vegetables or fruits was brought to each class for participants to taste. Also, at each of the four sessions, the participants were given handouts of the lessons, “challenges” for planning behavior changes, copies of recipes (including those tasted in class) in the HELP, and educational aids (e.g., refrigerator magnets of vegetables and fruits). For each group (one each from three counties), all lessons were taught in the same order by the researcher who used the same visuals, dishes to taste, and style of presentation. The control group did not receive the weekly lessons. However, after completing the post-test, they were offered a set of handouts and the HELP recipes. Pre- and post-tests, respectively, were administered to the control group from September through December 1998, with these results being used to test and retest the study instruments. The instruments tested reliably below .05, with the exception of the question that dealt with how long the reported number of vegetables had been eaten. This question, however, was accepted as reliable because of the slightly lower number of participants answering the question.

To consider this study quasi-experimental and a nonequivalent control-group design, we made efforts to select similar treatment and control groups. Analysis of the demographics conducted on treatment and control groups was only significantly different on one variable: how often they ate meals with someone else.

For the questionnaire Checkup on Your Good Eating Practices, we summed a score for each treatment and control group participant by using answers from seven questions related to fruit and vegetable behavior (total possible score of 28, after eliminating “doesn’t apply”). A paired-sample t-test was used to compare the means of the pre- and post-test scores for each group.

Post- and pre-test matched summed scores were also measured with a sign test. This test determined whether significant differences exist between positive and negative changes from the pre-test to the post-test. These changes, derived by subtracting pretest from post-test results, were placed into three categories: negative differences, positive differences, or ties (i.e., no change).

For the Stages of Change instrument, we used sign tests to measure differences of matched cases from pre-test to post-test administration, excluding “don’t know” for the number of servings, how long this amount of fruits and vegetables had been eaten, and for computed stages of change for fruit- and vegetable-eating behaviors for participants in both groups. An algorithm was used to calculate a separate stage of change for eating fruits and vegetables (box 1). Pre- and post-test fruit and vegetable stages were calculated for the treatment and control participants, except for those without sufficient data to categorize.


Sample Characteristics

Overall, the older adults in the treatment and control groups were similar. Seventy-six percent of the 50 participants in the treatment group were women, and 92 percent were White. Sixty-seven percent of the 51 participants in the control group were women, and 94 percent were White (data not shown).

Eating Practices

Results from the questionnaire entitled Checkup on Your Good Eating Practices showed that, compared with the control group, a significant difference existed between the means for the treatment group from the pretest to the post-test. From the pre- to the post-test, mean scores by the treatment group increased from 20.86 to 22.73 (p [less than or equal to] .05). For the control group, the means were 19.46 at the pre-test and 20.67 at the post-test (data not shown).

For the sign test, although two-tailed significance levels did not show a significant difference in either group’s summed scores, the percentages of negative and positive differences and the ties for the treatment group were noteworthy (table 1). From the pretest to the post-test, for example, 59 percent of changes by the treatment group were positive, compared with 43 percent of the changes by the control group that were positive. The percentage of ties (no change) was low for the groups (9 vs. 26 percent). These results imply that some type of change took place from pre-test to post-test administration, particularly in how members of the treatment group viewed their eating behaviors.

Stages of Change

Members of the treatment group categorized their fruit-eating behavior most often as Maintenance at the pre-test and post-test (32 percent each), followed closely by Precontemplation at pre-test and post-test (24 and 28 percent, respectively) and Preparation (20 percent each at pre-test and post-test) (table 2). Changes that could not be categorized dropped from 20 percent at pre-test to 4 percent at post-test. Responses reflective of behaviors in the Action category increased from 0 at pre-test to 8 percent at post-test; that is, at post-test, members of the treatment group consumed 3 to 4 or more servings of fruits each day and bad been consuming this amount for no more than 6 months.

Among the control group members, pre-test responses regarding their fruit-eating behaviors fell most frequently into Precontemplation, followed by Preparation and Maintenance (43, 25, and 20 percent, respectively). For this group, pre-test and post-test differences were minor among all categories.

For vegetable-eating behaviors, the treatment groups’ pre-test responses were mostly indicative of Precontemplation, followed closely by Maintenance, and then Preparation (30, 28, and 24 percent, respectively). That is, some members of the treatment group had not considered changing their vegetable-eating behavior, some had practiced changing their behavior, and others planned to take action during the next month to change their vegetable-eating behavior. At the post-test, members of the treatment group most frequently characterized their vegetable-eating behavior as being related to Maintenance, followed by Preparation, and Precontemplation (46, 26, and 12 percent, respectively), a different pattern than was the case at the pre-test phase. The control group’s responses at pre-test were mostly in two categories: Maintenance (47 percent) and Precontemplation (33 percent). The post-test category for Precontemplation remained at 33 percent, but the Preparation category was 18 percent, a change from the pretest (8 percent). Also, control group participants categorizing their behavior as Maintenance dropped to 33 percent at the post-test phase.

Results from the sign tests revealed no significant difference between pre-test and post-test results for neither the treatment group nor the control group for stage of change related to fruit-eating behaviors nor for the control group for stage of change related to vegetable-eating behaviors (table 3). However, a significant positive change for stage of change for the treatment group’s vegetable-eating behaviors existed. This positive change shows movement from a lower stage of change category to a higher category from the pre-test to the post-test.

Limitations of the Study

Findings were limited to the older adults in this study. Participants were not randomly selected because they were attendees of pre-arranged class sites, and some self-selection occurred.

Measurable behavior change may have been limited because of the short span of weeks in which treatment took place. Other considerations were (l) the environments of the congregate meal sites that varied in lighting, seating arrangements, distractions, and participant attentiveness and (2) the nutrition education on fruits and vegetables that the control group may have received from other sources prior to this study.


This study specifically examined the influence of nutrition education on the eating behaviors of older adults who resided in nonmetropolitan or semi-rural geographic areas and who were also participants of congregate meal programs. Based on recent trends, the nonmetropolitan or semi-rural older adult population is an important group to focus on because of factors such as the out-migration of younger persons in these areas and the sometimes-segmented nutrition and health care services (ADA, 2000; Rogers, 1999). Further study is recommended of not only this geographic audience but also of a comparison of this audience with urban older adults who participate in congregate meal programs.

Our findings indicate that the HELP nutrition lessons made a difference, measured by real and statistical significance, in how some older adults in the treatment group thought about changes, planned for changes, or made changes in their fruit- and vegetable-eating behaviors. Additionally, there is merit to the use and further study of the questions on the Stages of Change instrument for fruit- and vegetable-eating behaviors; that is, for the categorization of older adults’ behaviors into the Precontemplation, Contemplation, Preparation, Action, or Maintenance stages.

Realistically, diets vary over time because of a number of factors–one being changes in foods that are available. Therefore, a more relevant application of the Stages of Change construct, compared with simply measuring eating behavior, may be to measure cognitive and behavioral engagement. This approach allows researchers to focus more on what people are thinking about eating during the process of changing their diet, compared with measuring specific foods and nutrients consumed (Kristal, Glanz, Curry, & Patterson, 1999). This approach also may be more empowering to individuals who are working toward more healthful eating behaviors.

Table 1. Post-test/pre-test sign test for Checkup on Your

Good Eating Practices regarding fruit- and vegetable-eating

behaviors of elderly participants

Treatment group (1) Control group (2)


Negative differences 32 31

Positive differences 59 43

Ties 9 26

(1) n = 44.

(2) n = 49.

Table 2. Pre-test and post-test computed Stages of Change for

fruit- and vegetable-eating behaviors of elderly participants

Treatment group (1) Fruits Vegetables

Stage of change Pre-test Post-test Pre-test Post-test


Maintenance 32 32 28 46

Action 0 8 4 10

Preparation 20 20 24 26

Contemplation 4 8 0 0

Precontemplation 24 28 30 12

Cannot categorize 20 4 14 6

Control group (2) Fruits Vegetables

Stage of change Pre-test Post-test Pre-test Post-test


Maintenance 20 18 47 33

Action 2 6 0 4

Preparation 25 19 8 18

Contemplation 2 4 2 2

Precontemplation 43 49 33 33

Cannot categorize 8 4 10 10

(1) n = 50.

(2) n = 51.

Table 3. Post-test/pre-test sign test for Stages of Change computed

for fruit- and vegetable-eating behaviors of elderly participants

Treatment (1) Control (2)



Negative differences 24 16

Positive differences 22 20

Ties 54 64

Treatment (1) Control (2)



Negative differences 8 17

Positive differences 41 * 5

Ties 51 78

(1) n = 37 for fruit-eating behaviors, and n = 37 for vegetable-eating


(2) n = 45 for fruit-eating behaviors, and n = 41 for vegetable-eating


* Differences in behavior changes from the pre-test to the post-test

are significant, at p [less than or equal to] 05.


This educational program was mainly funded by a grant from USDA’s Extension Service and by partial support from the North Carolina Institute of Nutrition, Chapel Hill. This research also was supported by funds from the Dean’s Research Incentive Fund of the College of Human Ecology, The Ohio State University. We acknowledge the assistance of the staff of Ohio State University Extension in participating counties; those who assisted at the congregate meal sites; and M.A. (Annie) Berry, PhD, senior statistician of Ohio State University Extension.

(1) The data for this study were collected as part of the multi-State effort to test the lesson plans of the HELP.

(2) Ohio was selected to provide data from a nonurban population, as part of a coordinated effort to compare data among States.

(3) The size of the sample was based on guidance from the HELP Elderly Nutrition Education Coordinating Group: Mary P. Clarke, PhD, RD, Kansas State University; Sherrie M. Mahoney, MS, Kansas Extension Service; Jacquelyn McClelland, PhD. RD. North Carolina State University; William D. Hart, PhD, RD, St. Louis University; Denise Brochetti, PhD, Virginia Polytechnic Institute and State University; Alma Montano Saddam, PhD, RD, The Ohio State University.


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Cynthia A. Long, MS, RD

Ohio State University Extension–Crawford


Alma Montano Saddam, PhD, RD

The Ohio State University

Nikki L. Conklin, PhD

Ohio State University Extension

Scott D. Scheer, PhD

The Ohio State University and

Ohio State University Extension

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