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Correspondence: Address correspondence to Professor Geoffrey W. Greene, Department of Nutrition and Food Sciences, University of Rhode Island, 10 Ranger Road, 106 Ranger Hall, Kingston, RI 02881. E-mail: gwg{at}uri.edu
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Key Words: Dietary • Health promotion • Intervention • Stages of change • Transtheoretical model
The 2005 Dietary Guidelines for Americans (U.S. Department of Health and Human Services, U. S. Department of Agriculture, 2005), Healthy People 2010 (U.S. Department of Health and Human Services, 2000), and MyPyramid.gov (U. S. Department of Agriculture, 2005) emphasize the importance of fruit and vegetable intake for older adults and promote daily intakes (3.5–4.5 cups; 7–9 servings) that exceed previously recommended levels of at least 5 servings a day. Campaigns such as the 5 A Day For Better Health Program have encouraged greater consumption (Heimendinger, Van Duyn, Chapelsky, Foerster, & Stables, 1996). Since the program's inception there has been an increase in target awareness (Potter et al., 2000), as well as an increase of 3.7% in the proportion of adults meeting the criterion of 5 servings a day (Stables et al., 2002).
Consumption data indicate that the average older adult eats slightly more fruit and vegetables than do younger adults but still fall short of the new recommendations. Baseline data for the 5 A Day For Better Health campaign indicated that people over the age of 50 years reported a higher number of servings than did younger adults (3.6–4.1 vs 3.0–3.4; see Subar et al., 1995), and more recent data reveal that 36.8% of women 75 years of age or older met the criterion of 5 servings a day (Li et al., 2000).
Although the 5 A Day adult intervention studies were effective in increasing fruit and vegetable intake by approximately 0.5 serving (Potter et al., 2000), they did not specifically target older adults. For older adults, the intake of fruit and vegetables can be negatively influenced by a complex set of factors that distinguish them from younger adults. Changes in physical, economic, or cognitive status; management of multiple medical concerns and medication regimens; and shifts in social or family environments have all been identified as potential barriers to optimal food and nutrient intake (Fey-Yensan, English, Pacheco, Belyea, & Schuler, 2003; Position of the American Dietetic Association, 2005). The development of innovative, theory-based research may lead to the design of new methods of intervening at the community level to increase fruit and vegetable consumption in this population. Such research is particularly important, given the growing emphasis on promoting behavioral change in older adults as the key to healthier lifestyles and health outcomes (Center for the Advancement of Health, 2006).
The Transtheoretical Model of Behavior Change (TTM) is an integrative theory that uses individual decision-making processes as a basis to explain intentional behavior change (Prochaska, DiClemente, & Norcross, 1992; Prochaska & Velicer, 1997). The model consists of four interrelated dimensions: the central organizing construct; stage of change (the temporal dimension); and the three additional dimensions of decisional balance, self-efficacy, and process of change (Prochaska, Redding, & Evers, 2002). Previous cross-sectional research has found a strong effect of TTM variables on fruit and vegetable consumption in diverse populations, including older adults (Greene et al., 2004; Laforge, Greene, & Prochaska, 1994). The TTM has been used successfully to tailor health-promotion interventions to increase fruit and vegetable intake in young adults (Nitzke et al., 2007). However, to our knowledge, no study has looked at TTM-based interventions for fruits and vegetables in older adults.
Our primary purpose in this study was to determine the efficacy of a TTM-based intervention to increase fruit and vegetable intake in older adults. Our secondary purpose was to describe differences over 2 years in demographic and TTM variables comparing those who perceived they achieved the 5 A Day criterion with those who did not.
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Researchers recruited individuals into the SENIOR Project over a 12-month period through a variety of reactive recruitment methods, including newspaper and television advertisements, display tables that were set up at local supermarkets and pharmacies, and flyers and posters. In addition, community groups and organizations (such as the local senior center) assisted with recruitment. Researchers utilized proactive recruitment efforts (letters followed by phone calls) to recruit approximately 11% of the subjects (Saunders, Greaney, Lees, & Clark, 2003).
All data were collected in the participants' homes or in the SENIOR project office by trained interviewers. After completion of training, the interviewers administered a comprehensive questionnaire that included demographics and the instruments subsequently described here.
Intervention
The SENIOR Project's intervention components, which have been discussed elsewhere (Clark et al., 2002), included behavior-specific, TTM-based manuals, newsletters, expert system reports, and coaching calls subsequently described here. Researchers used monthly contact by mailings and phone calls to promote stage progression and maintenance.
Manual
After baseline assessment, participants received a fruit and vegetable manual that was organized by stages of change and by processes of change (behavioral strategies commonly employed in a specific stage to advance to the next). The manual also included recipes and tips for increasing fruit and vegetable intake. Individuals not receiving the fruit and vegetable intervention received either a manual about exercise or a fall-prevention manual, neither of which included information about nutrition.
Newsletters
Stage-based fruit and vegetable newsletters were sent to participants on a monthly basis except for Months 4, 8, and 12, when they received an expert system report (see the following paragraph). The newsletters included stories about older adults, practical tips, suggested activities, interactive sections, and recipes. The newsletters were stage based, designed to address stage-appropriate processes of change, and they included material to effect positive changes in self-efficacy and decisional balance. At Months 4 and 8, researchers rematched the newsletters to the participants' current stage of change.
Expert System Assessments and Reports
Using the baseline interview and telephone interviews (Months 4 and 8), researchers collected data on TTM variables (stage of change, decisional balance, processes of change, and self-efficacy) for the purposes of tailoring intervention materials. After each of these assessments, a computer-based expert system generated a four- to five-page report based on the participant's responses during the assessment. Reports were tailored for stage of change and also provided normative and ipsative feedback on TTM variables as well as fruit and vegetable intake. Expert system reports have been described elsewhere (Velicer, Prochaska, & Redding, 2006).
Coaching Calls
Participants received three 15-minute coaching calls by trained counselors over the 12-month intervention. Calls occurred approximately 4 to 6 weeks after the participants received the expert system report and used a standardized protocol incorporating motivational interviewing strategies in a stage-matched manner (Rollnick, Heather, & Bell, 1992). The counselors used the expert system reports to guide calls.
Instruments
Dietary Assessment
We assessed daily servings of fruits and vegetables by four methods using brief food-frequency types of instruments. The first two were based on the NCI Fruit and Vegetable Screener (Thompson et al., 2002), a nine-item instrument that includes respondent assessment of portion size. One can score the screener by using either respondent-assessed portion size (Frequency x Portion Size, or NCIP) or frequency alone (Frequency x 1, or NCIF). The third, used in 5 A Day (FAD) studies, is a seven-item instrument measuring frequency of intake and consumption of fruits and vegetables. However, it does not assess portion size or mixed dishes (Serdula et al., 1993). The fourth was a single-item screener (1S): "How many servings of fruits and vegetables do you usually eat each day? (a serving is 1/2 cup cooked vegetables, 1 cup of salad, a piece of fruit, 3/4 cup of 100% fruit juice)" (Laforge et al., 1994). This instrument had response categories from 0 to 6 or more.
A subsample of participants (n = 184) volunteered for additional telephone assessment of dietary intake. From 2 to 4 weeks following the initial in-person assessment including the instruments described herein, trained dietary recall interviewers called study participants on three nonconsecutive days to complete 24-hour dietary recalls using Minnesota Nutrient Database System software 4.03, version 31 (Nutrition Coordinating Center, Nutrition Data System for Research, Version 4.03_31, 1998–2002). Dietary recall interviews followed standardized telephone recall protocol that has been demonstrated to improve the accuracy of assessment (recalls were unannounced and included two weekdays and one weekend day; see Thompson & Subar, 2001). A supervisor reviewed all dietary recalls for quality assurance and extreme values, and the supervisor entered missing foods in consultation with University of Minnesota staff. We averaged the 1 to 3 days of recall prior to analysis. We calculated the servings of fruits and vegetables on the basis of gram weights consumed and U.S. Department of Agriculture portion sizes, using programs developed for the Behavioral Change Consortium Nutrition Working Group Dietary Validation Study (Greene et al., 2002).
Stages of Change
The stage of change instrument measures an individual's motivational readiness to eat 5 servings of fruits and vegetables a day (Laforge et al., 1994). It is a two-step algorithm consisting of a series of four possible questions that includes a skip pattern. All participants are asked initially to respond to the 1S concerning the number of servings of fruits and vegetables consumed per day. Participants who respond from 0 to 4 are asked, "Do you intend to start eating 5 or more servings of fruits and vegetables a day in the next 6 months?" Participants who respond "no" are classified in the precontemplation stage; those who respond "yes" are asked "Do you intend to start eating 5 or more servings of fruits and vegetables a day in the next 30 days?" Those responding "no" are classified in the contemplation stage; individuals responding "yes" are classified in the preparation stage.
Participants indicating that they are eating 5 to 6 servings are asked, "Have you been eating 5 or more servings of fruits and vegetables a day for more than 6 months?" Participants who respond "no" are classified in the action stage and participants responding "yes" are classified in the maintenance stage.
We defined stage progression over the 24-month study as follows: maintain = action or maintenance at 0, 12, and 24 months; no progress = preaction stages at 0, 12, and 24 months; progress = preaction at 0 and (or) 12 months and action or maintenance at 24 months; and relapse = action or maintenance at 0 and (or) 12 months and preaction at 24 months.
We defined stage of change for dietary fat as motivational readiness to reduce dietary fat intake according to the algorithm, "I consistently avoid high fat foods," with five response categories similar to the fruit and vegetable algorithm (Greene & Rossi, 1998).
Decisional Balance
The decisional balance instrument measures the importance that older adults assign to the pros and cons of making the decision to eat 5 servings of fruits and vegetables a day. This eight-item survey consists of two scales of four items, each representing the benefits (pros) and barriers (cons) of eating fruits and vegetables daily. Participants were asked to rate the importance of each statement in making the decision to eat fruits and vegetables according to a 5-point Likert scale (1 = not at all important; 2 = slightly important; 3 = moderately important; 4 = very important; 5 = extremely important). The pros are facilitators of change (reasons to eat fruits and vegetables). The cons are barriers (costs or difficulties involved in eating fruits and vegetables). Previous psychometric studies with older adults (N = 178) demonstrated that the measure is valid, with acceptable internal consistency for both scales (
=.79 for pros, =.75 for cons), with pro loadings ranging from.56 to.81 (M =.70) and con loadings ranging from.52 to.81 (M =.66; see Rossi et al., 2001).
Processes of Change
The processes of change instrument measures overt and covert strategies that older adults use to help themselves eat more fruits and vegetables. They tell us how people change their behavior and may involve the use of activities, thoughts, feelings, or events. Twelve strategies were identified in qualitative studies with older adults (Padula et al., 2003). Six experiential strategies are cognitive and affective in nature and have to do with thinking and feeling: consciousness raising ("learn something new"), dramatic relief ("your feelings count"), self-reevaluation ("look in the mirror"), self-liberation ("make a promise to yourself"), environmental reevaluation ("society will benefit if older adults eat more fruits and vegetables"), and social liberation ("look around"). Six behavioral strategies are performance oriented and focus on turning thoughts into action and getting support: helping relationships ("let others lend a hand"), reinforcement management ("give yourself a pat on the back"), interpersonal systems control ("spend time with people who eat fruits and vegetables"), counterconditioning ("use substitutes"), stimulus control ("take charge of cues and use reminders"), and planning ahead ("be proactive"). The 36-item instrument consists of 3 items per process. Participants were asked how often they thought, felt, or experienced the strategy described in each of 36 statements over the past month, according to a 5-point Likert scale (1 = never; 2 = seldom; 3 = occasionally; 4 = often; 5 = repeatedly). Previous measurement studies with older adults (N = 277) demonstrated that this measure is both valid and reliable, with alpha coefficients ranging from.72 to.84 and loadings ranging from.62 to.91, with the exception of the stimulus control subscale ( =.64, loadings.50–.72).
Situational Self-Efficacy
The situational self-efficacy instrument measures overall confidence older adults have in their ability to eat fruits and vegetables in challenging situations. This instrument consists of six items and provides a total score measuring global self-efficacy. Participants were asked to use a 5-point Likert scale (1 = not at all confident; 2 = not very confident; 3 = moderately confident; 4 = very confident; 5 = extremely confident) to rate each statement with regard to how confident they would be in eating vegetables and fruits in each of the situations presented. Previous instrument-development studies with older adults (N = 177) demonstrated that this measure is both valid and reliable, with excellent internal consistency ( =.89) and loadings ranging from.59 to.87 (M =.75; see Rossi et al., 2003).
Analyses
We conducted our analyses by using the Statistical Package for the Social Sciences (SPSS for Windows, Version 14, SPSS, Inc.). Prior to our analyses, we transformed (square root transformation) servings of fruits and vegetables for all instruments (medians, untransformed means, or both, plus or minus standard deviation, are reported to facilitate interpretation). We analyzed differences by data completeness by using t tests or chi-square analyses. We assessed comparisons between intervention and control groups by using analysis of variance for repeated measures, and we did the same for differences between stage-progression groups. Our intent-to-treat analysis (analysis of variance for repeated measures) used the last recorded value for participants failing to provide dietary data at follow-up. We compared differences in TTM variables between stage-progression groups by using analysis of covariance with baseline scale score as a covariate. The baseline dietary validation substudy used Pearson's correlations and t tests comparing recall to instruments and t tests comparing preaction with postaction groups within an instrument.
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2 =.004, and NCIP, F( 1, 1206) = 3.84, p =.050, 2 =.003, but not for the NCIF, F(1, 1251) = 0.98, p =.322, 2 =.001, or 1S, F(1, 1274) = 1.67, p =.196, 2 =.001.
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=.703, F(30, 2132) = 13.72, p <.001, multivariate 2 =.162, and significant univariate effects (adjusted for baseline scale score) for self-efficacy and processes, except for self-liberation and social liberation and not for pros and cons (Table 4). Effect sizes were in the medium range for self-efficacy and in the small-to-medium range for processes. The maintain group demonstrated higher process use and greater self-efficacy than the fail to progress and relapse groups and did not differ from the progress group, with the exception of self-efficacy.
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The large proportion of participants perceiving that they consumed at least 5 servings of fruits and vegetables a day at follow-up (78%) may limit generalizability of the study, but it provides an opportunity to explore stage stability and change over 2 years in older adults. We based stage classification on the single-item respondent assessment of usual intake. This item was not sensitive to change in this sample, unlike the results of Resnicow and colleagues (Resnicow et al., 2001), and this may be related to the sample's relatively high intake at baseline and the truncation of the measure at 6 or more servings. The measure also underestimated intake at baseline. Nevertheless, Table 5 demonstrates that stage classification based on meeting or not meeting the FAD criterion using this measure reflected substantial differences in intake measured by recall and other instruments. Participants who maintained a postaction stage for 24 months consumed between 2 and 4 more servings of fruits and vegetables a day than those failing to progress.
To our knowledge, this is the first study to explore stage progression for fruits and vegetables over 24 months while simultaneously measuring TTM variables. Our previous study (Greene et al., 2004) found baseline differences in TTM variables between stages that were similar to those for other health behaviors (DiClemente et al., 1991; Greene et al., 1999; Prochaska et al., 1992, 1994; Prochaska, Velicer, Guadagnoli, Rossi, & DiClemente, 1991). Pros, process use, and self-efficacy were lowest in precontemplation, increased in contemplation or preparation, and continued to increase in action or maintenance, especially for behavioral processes and self-efficacy. This study found that TTM variables differentiated between stage-progression groups, with the maintain and progress groups differing from the relapse and fail to progress groups—with the exception of decisional balance. The pattern of TTM variable differences in this study was similar to that of longitudinal studies for self-efficacy (Campbell et al., 1999), but it differed for decisional balance (Norman, Norman, Rossi, & Prochaska, 2006; Prochaska et al., 1994)—which may not be relevant for participants in postaction stages of change (Prochaska et al., 1992). The strongest effect size for any process was for the behavioral process of stimulus control, but the second strongest effect was for the experiential process of self-reevaluation. These results suggest that, for an acquisition behavior such as eating fruits and vegetables, behavioral processes and self-efficacy are important, but experiential processes continue to be important for the maintenance of healthful eating habits.
Although 25% of the population was 80 years of age or older at baseline, there was no effect of age on stage progression. In addition, there were no effects of gender, income, or marital status on stage progression. It is interesting that the 58% of the sample who maintained 5 or more servings for 24 months had a lower body mass index at follow-up than did other groups. This finding supports other data that have found a protective effect of fruit and vegetable consumption on obesity (Rolls et al., 2004). There was, however, no effect of fruit and vegetable consumption on weight changes over 24 months, which is fortunate because other researchers have found that weight loss in an older population is associated with a substantial increase in mortality (Newman et al., 2001). Maintainers were more likely to perceive that they were avoiding fat than those who failed to progress. This suggests that perceived maintenance of a high fruit and vegetable intake is associated with other health-promoting dietary behaviors. Finally, maintainers were more likely to perceive their health as good to excellent than were those who failed to progress. The effect of perceived health on ability to consume a healthful diet in older adults is an important area for future research.
In conclusion, this TTM-based intervention was effective in increasing fruit and vegetable intake in older adults. Although we confirmed effectiveness by using an intent-to-treat analysis, demographic differences between those individuals with and without complete data suggest limits to the generalizability of this primarily print-based intervention. This study provides evidence that TTM constructs are valid and can be used by practitioners for interventions with older adults. Given the importance of fruits and vegetables in reducing chronic disease risk, policy implications of this study are that (a) additional research is needed to translate study findings to more diverse groups, and (b) contextual factors such as cost and availability of fresh produce may have to be addressed in interventions that will effectively move older populations toward the 2005 Dietary Guidelines recommendations.
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1 Department of Nutrition and Food Sciences, University of Rhode Island, Kingston.
2 College of Nursing, University of Rhode Island, Kingston.
3 Department of Psychology, University of Rhode Island, Kingston.
4 Program in Gerontology and Rhode Island Geriatric Education Center, University of Rhode Island, Kingston.
Decision Editor: William J. McAuley, PhD
Received for publication January 4, 2007. Accepted for publication March 29, 2007.
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