Health Education Research, Vol. 18, No. 2, 224-236,
April 2003
© 2003 Oxford University Press
Assessing stages of change for fruit and vegetable intake in young adults: a combination of traditional staging algorithms and food-frequency questionnaires
Department of Nutritional Science and Dietetics, University of Nebraska–Lincoln, 307 Ruth Leverton Hall, Lincoln, NE 68583-0806, 1 Nutrition and Food Service Management, Syracuse University, 034 Slocum Hall, Syracuse, NY 13244-1250, 2 Nutrition and Food Management, Milam Hall 108, Oregon State University, Corvallis, OR 97331-5103, and 3 Department of Food Science and Human Nutrition, University of Maine, 5749 Merrill Hall, Orono, ME 04469-5749, USA
Correspondence to: N. M. Betts. E-mail: nbetts1{at}unl.edu
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Abstract |
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Adequate fruit and vegetable intake is strongly associated with a reduced risk for various chronic diseases. US national surveys show that 18- to-24-year-olds are not consuming enough of these foods. Theory-based nutrition interventions, e.g. stage-tailored education programs, are needed for promoting fruit and vegetable consumption in this age group. Accurate stage assignment is the basis for developing effective stage-tailored interventions. In the current study, three different methods were compared for assigning stages of change in fruit and vegetable intakes by young adults. Significant differences in food intake, decisional balance and self-efficacy were found between respondents with concordant responses to the traditional stage algorithm and the food-frequency questionnaire (FFQ) and those with discordant responses. The stage assignment method that combined the staging algorithm and FFQ identified a distinct stage, labeled ‘non-reflective action’, in addition to the traditional five stages of change. This stage lay between the preparation and action stages with regard to food intake and psychosocial variables. Implications of the findings were discussed for future intervention programs that attempt to promote fruit and vegetable intake.
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Introduction |
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Numerous studies have shown a strong association between fruit and vegetable consumption and lower risk for various chronic diseases (e.g. cancer and cardiovascular disease), and evidence continues to accumulate (US Department of Health and Human Services, 1988
, 2000; National Research Council, 1989; Block et al., 1992; Steinmetz and Potter, 1996). Adequate consumption of fruits and vegetables is recommended in several US dietary guidelines (Butrum et al., 1988; US Departments of Agriculture and Human Nutrition Information Service, 1992; US Departments of Agriculture and Health and Human Services, 2000). A national ‘5-A-Day for Better Health’ campaign was launched in 1991 for promoting daily consumption of at least five servings of fruits and vegetables (Havas et al., 1995). However, recent studies show that the typical American diet still falls short of these recommendations (Serdula et al., 1995; Krebs-Smith et al., 1997; Trudeau et al., 1998; Li et al., 2000). Translating scientific knowledge and dietary recommendations into actual changes in behavior has been difficult, and is a major challenge for the nation.
Dietary habits of young adults between ages 18 and 24 years have increasingly drawn researchers’ attention. This age group is in transition from adolescence to adulthood and has the potential to influence the health status of the next generation. Previous literature revealed that young adults tended to consume excess amounts of total fat, saturated fat, cholesterol and sodium (Hubert et al., 1987; Block et al., 1988; Bull, 1988; Georgiou and Arquitt, 1992; Hampl and Betts, 1995). Other studies have found inadequate intakes of essential micronutrients, such as calcium, iron, zinc, folate, and vitamins A, B6 and C (Hoffman, 1989; US Department of Agriculture, 1988; Zive et al., 1996), as well as low consumption of fruits and vegetables (Hernon et al., 1986; Hoffman, 1989; Skinner, 1991; Mitchell et al., 1994; Ma and Betts, 1998; Song et al., 1996). Promotion of dietary change among young adults, who are currently healthy, could be especially challenging. Potential future health benefits of a balanced diet, which are the principal emphases in many of the current guidelines, may not seem as appealing to this particular population as immediate physical and psychological benefits.
Theory-based interventions have shown promise for promoting desirable changes in eating behaviors (Glanz and Eriksen, 1993; Campbell et al., 1994; Sorensen et al., 1996; Glanz et al., 1998). The Transtheoretical Model (TTM), an innovative and comprehensive framework, conceptualizes when and how behavior change occurs (Prochaska and DiClemente, 1982; Prochaska and Velicer, 1997). The stages of change construct, the core of the TTM, proposes that behavior change is a dynamic process with five distinct stages on a temporal continuum: (1) precontemplation—unaware a problem exists and/or not considering changing the behavior, (2) contemplation—thinking about changing, (3) preparation—planning to change in the immediate future and may have made small attempts, (4) action—has changed problem behavior in the short term (within the past 6 months), and (5) maintenance—problem behavior changed for at least 6 months. Individuals progress through these stages by implementing cognitive, experiential and/or behavioral activities, as entailed in the processes of change construct in TTM. In addition, changes in decision making (balance between the pros and cons of changing) and self-efficacy (confidence in performing specific tasks leading to change) likely occur in the process of behavior change.
The model has proven to be effective as a basis for developing interventions for changing addictive behaviors, especially cigarette smoking (Prochaska et al., 1988, 1992; DiClemente et al., 1991; Herrick et al., 1997). Only more recently has the model been applied to promotion of dietary behavior change, such as dietary fat reductions (Curry et al., 1992; Glanz et al., 1994; Greene et al., 1994; Sporny and Contento, 1995; Hargreaves et al., 1999; Finckenor and Byrd-Bredbenner, 2000), dietary fiber consumption and fruit and vegetable intake (Campbell et al., 1994, 1999; Glanz et al., 1994, 1998; Laforge et al., 1994; Domel et al., 1996; Sorensen et al., 1996; Brug et al., 1997; Cullen et al., 1998).
The most critical aspect of using TTM for developing interventions is accurate staging (Ni Mhurchu et al., 1997; Lechner et al., 1998; Greene et al., 1999). Stage of dietary change has been commonly assessed with the use of a one- to five-item staging algorithm similar to algorithms used for staging smoking cessation (Prochaska and DiClemente, 1982; McConnaughy et al., 1983, 1989; DiClemente et al., 1991; Prochaska et al., 1992; Prochaska and Velicer, 1997). However, measuring the target behavior of smoking cessation is relatively clear-cut compared to measuring eating behaviors. Measuring dietary intake of foods like fruit and vegetables is especially problematic because serving size must be accounted for and serving size estimation can be a daunting task. Lechner et al. and Ni Mhurchu et al. draw attention to the possibility for significant discrepancies between self-rated food intakes and those measured using objective methods (Lechner et al., 1997; Ni Mhurchu et al., 1997). Study of the concordance/discordance between self-rated and measured dietary change is essential to application of TTM. This research sought to determine if staging readiness to increase fruit and vegetable intake differs when the target behavior is self-rated on a traditional staging algorithm or measured objectively. The affect that staging differences have on other components of TTM was also examined.
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Methods |
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Sample
Research participants were from 10 states in a USDA/CREES regional research project, NC219, including Alabama, Kansas, Maine, Michigan, Nebraska, New York, Rhode Island, Oregon, South Dakota and Wisconsin. All states collaborated in developing and following the research protocol. The protocol was reviewed by the institutional review board in each state and was granted approval.
Names, addresses and telephone numbers of potential respondents were randomly selected from purchased lists. Trained interviewers made up to four attempts to contact each selected person by telephone and were successful in reaching 3570 individuals who fit the 18–24 year age criterion for a 33% contact rate (The American Association for Public Opinion Research, 2000). Of those, 640 (14%) refused to participate. The remaining 2930 who agreed to participate completed a brief screening interview that staged them in terms of their fruit or vegetable intake with the food group chosen at random. Questionnaires were mailed to the 2930 followed by reminder postcards and a second survey mailed after 10 and 20 days, respectively, to maximize response rate. An incentive drawing of $25 per 25 returned surveys was completed in each state. In all, 1545 usable surveys were returned for a 55% response rate to the mail survey (American Association for Public Opinion Research, 2000). Compared with respondents to the mail survey, non-respondents were more likely to be male and in the precontemplation stage.
Measures
A set of questionnaires was developed and validated through extensive pre- and pilot-testing with young adults.
Demographic characteristics
Demographic variables included age, gender, dwelling place, household size and number of children, marital status, student status, education, and race/ethnicity. Respondents were also asked whether they smoked cigarettes or drank alcohol.
Fruit and vegetable intake
A semi-quantitative food frequency questionnaire (FFQ), measuring fruit and vegetable intake separately, was originally adapted from the National Cancer Institute’s Health Habits and History Questionnaire (Thompson et al., 1994). Individual in-depth interviews and focus groups with young adults were conducted to ensure appropriate inclusion of foods eaten by the target age group. The questionnaire was condensed to 12 fruit items and 14 vegetable items through pilot trials. To validate the FFQ, convenience samples of young adults were recruited to complete the FFQ along with 3-day food records (n = 30) and 24-h recalls (n = 119). The numbers of servings and types of fruit and vegetables reported in the records and recalls were compared with the frequency of intake of items reported on the FFQ. In the 3-day records, 77% of the young adults reported consuming fruit and vegetable items that matched the items listed on the FFQ at least 80% of the time. Correlations between the numbers of servings on the 24-h dietary recalls and the frequency of consumption were r = 0.43, P < 0.04 for fruit and r = 0.65, P < 0.001 for vegetables. In addition, two states, Nebraska and Oregon, compared responses to a seven-item fruit and vegetable screener developed for the national 5-A-Day for Better Health program (Thompson et al., 1994; Eldridge et al., 1998; Hunt et al., 1998) with the FFQ (n = 372). Correlations between consumption frequency reported on the FFQ developed for the current study and the seven-item screener were r = 0.76, P < 0.001 for fruit and r = 0.71, P < 0.001 for vegetables.
In the questionnaire, frequency of consumption was measured on an eight-level rating scale from ‘one time per month or less’ to ‘twice per day or more’. Three options of serving sizes (i.e. small, medium and large) were provided. For most foods, a medium serving was comparable to the Food Guide Pyramid (FGP) serving (US Departments of Agriculture and Human Nutrition Information Service, 1992). However, the FGP serving sizes are intended for use in nutrition labeling and several food item servings sizes differ significantly from the portion sizes people typically consume. Typically consumed portion sizes as obtained from USDA were listed for the following foods: dried fruits (1/2 cup), catsup and salsa (1/4 cup), and vegetable soup/stew (3/4 cup).
Stages of change
Stage of change was assessed for fruits and vegetables separately via a four-item algorithm which was constructed based on previous research (Campbell et al., 1994, 1999; Glanz et al., 1994, 1998; Sorensen et al., 1996; Brug et al., 1997; Cullen et al., 1998). The first item asked how many servings of fruits/vegetables the respondent usually consumed each day. If the respondent reported consuming <2 servings for fruit and <3 servings for vegetables, (s)he was directed to an item asking about intention to eat 
2 servings a day of fruit or 3 or more servings a day of vegetables in the next 6 months or 30 days. If the respondent reported consuming 2 servings a day of fruit or 3 servings a day of vegetables, (s)he was directed to an item asking whether (s)he had been doing so for more than 6 month, followed by a fourth item asking his/her intention to eat more in the next 6 months or 30 days.
Self-efficacy and decisional balance
Twenty self-efficacy items and 63 decisional balance items were first generated as a result of literature review and in-depth individual interviews with convenience samples of young adults. Items were deleted or revised through pilot-tests according to results from principal component analysis, inter-item correlation coefficients and Cronbach’s 
coefficients. A final five-item, five-point Likert scale ranging from very confident to not confident at all assessed self-efficacy about performing tasks involved in consuming the recommended numbers of servings of fruits and vegetables. Cronbach’s coefficients of the self-efficacy scale were 0.86 for fruit and 0.85 for vegetables. The final decisional balance scale consisted of 10 con and eight pro items. Respondents rated how important each item was in their decision making for consuming fruits and vegetables. The five-point importance scale ranged from very important to not at all important. The overall decisional balance scale had a Cronbach’s coefficient of 0.80 for fruit and 0.79 for vegetables; the coefficients of the pro and con items were 0.73 and 0.72 for fruit and 0.73 and 0.70 for vegetables, respectively.
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Data processing and statistical analysis |
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Data processing
The FFQ data were recoded to reflect FGP servings for all food items. Total daily servings of fruits and vegetables, separately, were derived by summing the product of frequency of consumption and serving size across all the food items listed. Daily intakes of fruits and vegetables were both positively skewed and therefore transformed to conform more closely to a normal distribution using the natural logarithm [z = ln (x + 1), where x is the total fruit/vegetable servings computed from the FFQ] for analytical purposes. Least-squares adjusted means and standard deviations (SD) were calculated from log-transformed data [(ez - 1); SD = (ez - 1) SD (z)]. The self-efficacy and decisional balance scales were recoded to not at all confident/important (1) to very confident/important (5). Self-efficacy scores, pro and con scores were the summation of individual responses to the respective scale items; then, they were converted to standard scores (mean = 50, SD = 10).
Stages of change in fruit and vegetable intake, respectively, were assigned in three ways. Stage assignment A, the traditional staging classification (Campbell et al., 1994, 1999; Glanz et al., 1994, 1998; Sorensen et al., 1996; Brug et al., 1997; Cullen et al., 1998), grouped respondents into one of five stages as follows: When self-rated intake was <2 servings a day for fruit or <3 servings a day for vegetables with no intention to eat more the classification was precontemplation. Those reporting <2 servings a day for fruit or <3 servings a day for vegetables who intended to eat more in the next 6 months were classified in contemplation and those who intended to eat more in the next 30 days were classified in preparation. Classification into action occurred when the respondent reported consuming 2 servings a day of fruit or 3 servings a day of vegetables for less than 6 months and in maintenance if consuming the higher amounts for more than 6 months.
In Stage assignment B, the number of daily servings of fruit and of vegetables as reported on the FFQ was compared to the self-rated intake on the staging algorithm. As stated above, respondents in Stage assignment A were classified into action or maintenance when they reported that they consumed 2 servings a day of fruit or 3 servings a day of vegetables. However, if their FFQ results indicated that they consumed <2 servings a day of fruit or <3 servings a day of vegetables (discordant), they were reclassified. Their response to the item asking about intention to eat more determined the stage for reclassification. Twenty-nine persons (1.9%) did not respond to this item and were excluded from further analysis. The remaining respondents were reclassified into precontemplation if they reported having no intention of consuming 2 servings a day of fruit or 3 servings a day of vegetables fruits or vegetables, contemplation if they reported an intention to consume 2 servings a day of fruit or 3 servings a day of vegetables within the next 6 months and preparation if they reported an intention to consume 2 servings a day of fruit or 3 servings a day of vegetables within the next 30 days.
Stage assignment C reclassified respondents who reported that they consumed <2 servings a day for fruit or <3 servings a day for vegetables on the traditional staging algorithm, but reported 2 servings a day of fruit or 3 servings a day of vegetables on the FFQ (discordant). Because these respondents were unaware that they were consuming the recommended amounts of fruit or vegetables, they were grouped into a separate stage that was labeled non-reflective action. Non-reflective action indicates that the young adult is not thinking about what (s)he is consuming.
Analysis
All analyses were performed using SPSS for Windows, version 8.0. One-way analysis of variance (ANOVA) and Student’s t-test examined differences in fruit and vegetable intakes among subgroups classified according to demographic characteristics. Scheffe’s post hoc test was performed when appropriate. Student’s t-test examined differences in fruit servings, vegetable servings, self-efficacy, and standard pros and cons scores between respondents whose reported food intakes from the FFQ were concordant with their self-identified stages according to assignment A and those that were discordant. After controlling for demographic variables, univariate ANOVA with Bonferroni correction determined differences in food intake, self-efficacy, pros and cons by stage according to each of the three assignment methods.
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Results |
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Of the 1545 survey respondents, 61% were females, 90.3% whites, 49.7% current students (39.4% full-time and 10.3% part-time), 28.2% married or living with a partner and 23.3% having children in household. Approximately 48% resided in a city, 26% in a suburb and 25% in a rural area. Only 2.1% of the respondents had less than high school education; 27% were high school graduates and 69% attained at least some post-secondary education. As shown in Table I
, fruit intake was significantly different by dwelling place, marital status and cigarette smoking status; vegetable intake differed significantly by dwelling place, presence of children in the household and race/ethnicity. Neither fruit intake nor vegetable intake differed significantly by age, gender, household size, education level or student status.
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Daily fruit servings estimated from the FFQ were discordant with the self-identified stages by method A for approximately 22% of those in precontemplation, 33% in contemplation, 43% in preparation, 21% in action and 14% in maintenance. The discordance ratios for vegetables were 26% in precontemplation, 37% in contemplation, 39% in preparation, 50% in action and 29% in maintenance. Considerable differences in food intake, self-efficacy, pros and cons were found between respondents whose total FFQ servings were concordant with their self-identified stages and those with discordant responses (Table II
). With few exceptions, fruit and vegetable servings as well as self-efficacy were significantly lower for those with concordant responses between the two measures within the precontemplation, contemplation and preparation stages, respectively. The opposite was true for respondents in the action and maintenance stages. Of note, self-efficacy was consistently higher for those who ate more fruits and vegetables across the five stages as well as between concordant and discordant respondents within each stage. In addition, the respondents who self-staged in preparation but reported consuming 2 servings a day of fruit on the FFQ had significantly higher pros than those with <2 fruit servings. Self-staged contemplators who consumed 3 servings a day of vegetables had significantly higher pros and lower cons than the contemplators who ate <3 servings. The pros and cons for vegetable consumption were both significantly higher for those in the action stage who reported 3 servings a day on the FFQ compared with those with <3 servings a day.
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As shown in Table III
, method A, the assignment strictly based on individuals’ self-classification placed the greatest proportion of respondents in maintenance and approximately even proportions in the other four stages for fruit intake. This method resulted in almost even distribution among the stages for vegetable intake, except only 3% of the respondents were in the action stage. Method B caused approximately 2 and 7% reductions in the action and maintenance stages for fruit intake, and 1.5 and 7.5% reductions in the same stages for vegetable intake. These reductions were redistributed mostly to the precontemplation and preparation stages for both fruits and vegetables. The non-reflective action stage in method C was composed of respondents redistributed from the preparation, contemplation and precontemplation stages in method B. The proportions of respondents who were reclassified in the non-reflective action stage were as follows: preparation (fruit 6%; vegetables 6.1%), contemplation (fruit 4.3%; vegetables 7.8%) and precontemplation (fruit 3.2%; vegetables 6.0%). This non-reflective active stage accounted for 14.5 and 24.4% of the total respondents for fruit and vegetable intakes, respectively.
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Table III
indicates an incremental trend for both fruit and vegetable intakes from precontemplation to maintenance according to all three assignment methods. Respondents in the action and maintenance stages from all three methods and additionally those in the non-reflective action stage from method C reached the FGP recommendations for fruits and vegetables.
Table III also shows the patterns of changes in self-efficacy and decisional balance by stage were notably similar across the three assignment methods and for both food groups. Self-efficacy increased successively along the stages. Precontemplators and contemplators had significantly lower self-efficacy regarding increasing fruit and vegetable intakes than those in the action and maintenance stages in all three methods and those in non-reflective action in method C. Precontemplators and contemplators for both food groups in all three methods as well as those in the non-reflective action stage for fruit intake had lower pros versus cons, while the opposite was true for respondents in other stages. The crossover point between the pros and cons, i.e. the point at which the pros surpassed the cons, occurred between contemplation and preparation. With few exceptions, the largest positive differences between the pros and cons were seen in the action stage.
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Discussion |
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The stages of change construct, the core of the TTM, has recently received increasing interest from researchers in the nutrition field. Assignment to appropriate stage of change is a key requirement for the development of effective stage-tailored interventions (Prochaska et al., 1993
; Velicer et al., 1993; Campbell et al., 1994; Pallonen et al., 1994, 1998; Green et al., 1999; Finckenore and Byrd-Bredbenner, 2000). The staging algorithm method developed by Prochaska and DiClemente for smoking cessation has been widely adopted in studies of other health problem behaviors and appears to have become a customary method for assessing stage of change (Prochaska and DiClemente, 1983). However, dietary behaviors are more complex than smoking cessation with regard to many aspects such as continuity, variability as well as essentiality and risk for health (Bowen et al., 1994; Glanz, 1997; Greene et al., 1999). The validity of staging algorithms for dietary behaviors depends largely on people’s ability to accurately perceive and evaluate their own diets, which in turn are determined by individuals’ knowledge, attitudes and beliefs concerning the behavior. Unfortunately, public knowledge and self-awareness about nutrition and diet appears to be at a lower level compared with that about other lifestyle behaviors (Bowen et al., 1994).
In their review, Ni Mhurchu et al. argued for a combination of a dietary assessment method and a staging questionnaire for assessing stages of dietary change (Ni Mhurchu et al., 1997). Dietary fat intake is the one eating behavior that has been extensively examined using the stages of change model (Curry et al., 1992; Glanz et al., 1994; Greene et al., 1994, 1999; Sporny et al., 1995; Hargreaves et al., 1999; Finckenor and Byrd-Bredbenner, 2000). Objective behavioral markers that improve the accuracy of stage assignment have been developed for this particular dietary behavior (Greene et al., 1994, 1999; Hargreaves et al., 1999). Subjects’ self-classified stages by the stage algorithm were re-evaluated based upon their responses to the behavioral markers and stage assignment was adjusted accordingly. The adjustment improved the determination of stages of change in dietary fat intake.
No such indicator device is currently available for assessing changes in fruit and vegetable intakes, separately or in combination. Currently, and in past studies (Campbell et al., 1994, 1999; Glanz et al., 1994, 1998; Brug et al., 1997), fruit and vegetable intakes were assessed with FFQs that varied in the number of food items included. Cullen et al. employed a food recognition form that lists a number of fruit and vegetable items and respondents were asked to check the items they consumed in the past 24 h (Cullen et al., 1998); each checked item was coded as one serving and the sum gave the total serving. The 24-h dietary recall method was used in Laforge’s study (Laforge, 1994). In previous literature, researchers stated that even though there are more accurate and valid dietary assessments available, FFQs or 1-day food recalls may be suitable and adequate for large population samples and for between-group comparisons (Margetts and Nelson, 1991; Ni Mhurchu et al., 1997). In only one previous study (Lechner et al., 1998), stage assignment was adjusted for fruit and vegetable intakes by incorporating estimates from objective dietary assessments by FFQs; however, adjustments may indeed be necessary as indicated by the current findings.
For a noteworthy proportion of our respondents, self-rated daily intakes of fruits and vegetables in the stage algorithms differed from the total numbers of servings reported on the FFQs as compared to the behavioral criteria, i.e. 2 servings a day for fruits and 3 servings a day for vegetables. Part of our preliminary data collected while attempting to validate the FFQ indicated that self-rated intakes varied more from 24-h recalls during telephone dietary interviews than from the FFQ. Selfassessment tended to underestimate fruit and vegetable intake compared with both the 24-h recalls and the FFQ (fruit: 1.8 ± 0.9 versus 2.2 ± 0.9 and 3.1 ± 1.8; vegetables: 2.4 ± 1.2 versus 4.2 ± 2.1 and 4.4 ± 1.7). Individuals’ misconceptions of their own food intake may be attributed to common confusion over serving sizes, portion sizes and food label servings. This confusion may greatly bias a person’s self-rated food intake when information regarding standard or relative serving size is not provided. Currently, the portions of food served in many commercial eating establishments are larger than the FGP recommended sizes. This social phenomenon may partially explain why many of our respondents self-perceived their intakes as being lower than the behavioral criteria while their FFQs suggested otherwise. As with Lechner et al. (Lechner et al., 1998), we found that misclassification of stage appears to be more substantial for vegetable intake than for fruit intake. This finding could be because vegetables are more often eaten in mixed dishes than fruit and therefore more difficult to conceptualize in terms of serving size.
Overestimation of servings may be due to social desirability and certain psychosocial traits such as positive attitudes and high self-efficacy (Lechner et al., 1997). Adding support to the findings of Lechner et al. (Lechner et al., 1997), we discovered significant differences in self-efficacy and decisional balance between the respondents whose self-identified stages by the stage algorithms alone were in concordance with their estimated intakes by the FFQs and those with discordant responses to the two measures. These findings suggest that multiple sources of external and internal factors may contribute to a person’s misjudgment, either under- or over-estimation, of his/her own food intake. Misclassifications due to ambiguity involved in self-rated intake may be adjusted by incorporating estimates from an objective dietary assessment such as an FFQ. It is well known that developing a valid and reliable objective dietary assessment is a challenging task. Research building upon existing studies of dietary measures is greatly needed for developing an objective behavioral marker of dietary intake.
While our findings argue for the need to incorporate an objective dietary assessment for more appropriate staging, our data are limited in the ability to determine relative validity of the three methods. Both fruit and vegetable servings assessed by the FFQ increased linearly from precontemplation to maintenance by all three methods. The patterns of changes in self-efficacy and decisional balance according to the three stage assignment methods resembled one another and all agree with what were proposed in the original theory (Prochaska and Velicer, 1997). We also found higher percentages of the variance in fruit and vegetable intake explained by stage of change according to methods A, B and C, respectively (data not shown). The above suggests construct validity of all three methods and efforts are currently underway using longitudinal research to better determine the relative validity of the three staging methods.
Our findings agree with those of Lechner et al. (Lechner et al., 1998) who found a considerable proportion of respondents had self-classified into maintenance and, to a lesser degree, action, but did not meet the behavioral criteria. They speculated that these people mistakenly considered themselves to be meeting the dietary recommendation levels as a result of previous attempts at increasing their consumption. This misconception caused these people to be unlikely to contemplate further change. Our study included an item on the algorithm that asked those in action and maintenance whether they intended to consume more fruit and vegetables along with the 6-month and 30-day time frame for those responding ‘yes’. This allowed us to reclassify individuals into the pre-action stages. While reclassification had little effect on the mean servings of fruit and vegetables for those in precontemplation, contemplation and preparation, it had a significant effect on mean servings for those in action and maintenance. The mean servings of fruit and vegetables increased after reclassification, suggesting that staging algorithms that do not account for discrepancies between self-rated and measured food intake may result in significant bias.
We also reassigned those self-classified in the pre-action stages, but who reported consuming the recommended amounts of fruit and vegetables on the FFQ. We labeled this stage non-reflective action because individuals in the stage were those who failed to realize that their fruit and vegetable intakes already met the respective criteria regardless of their intentions of changing. The non-reflective action stage was composed primarily of respondents who self-classified themselves in preparation and contemplation. Fruit and vegetable servings, self-efficacy scores, and pro and con scores for respondents in this stage fell in between those of the preparation and action stages. Therefore, it is possible that these individuals intended to eat the recommended servings but lacked an understanding of portion size. Prochaska and Velicer stated that TTM should not be considered a ‘closed model’ and that modifications are needed to address unique aspects of behaviors such as dietary intake (Prochaska and Velicer, 1997). Ni Mhurchu et al. agreed and added that adaptation or expansion of the current five-stage model may be particularly necessary for dietary change because of its inherent complexity (Ni Mhurchu et al., 1997). Our finding of a non-reflective action stage needs to be tested in future research in different populations and with other eating behaviors as well as in longitudinal investigations. This line of research is warranted because of the importance of accurate staging for applying other TTM constructs, and for designing and targeting nutrition intervention programs.
Of note, caution is needed in generalizing these findings. Comparisons of respondents and non-respondents indicated a possible under-representation of men and precontemplators among the respondents. Avenues need to be explored for better recruitment of those individuals.
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Implication for research and practice |
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Accurate dietary assessment along with selfindicated readiness to change is key to correct stage assignment, which in turn may be important for developing effective nutrition intervention, and predicting changes in dietary and psychosocial measures. The current study adds support to the existing evidence that shows the usefulness of the staging algorithm method for assessing stage of dietary change. However, it appears to be necessary to adjust stage assignment with the use of more objective dietary assessment results. In addition, the current stages of change model needs to be extended because of the complexity of dietary change. If indeed, there is such a stage as non-reflective action for describing dietary change, individuals may be more receptive to tailored intervention messages when targeted as a separate group compared to being mixed within other groups. As a result, greater effectiveness could be achieved. The existence of such a stage and the development of specifically tailored interventions need to be investigated in future research and practice. Taking fruit and vegetable intake as an example, intervention messages tailored towards the non-reflective action stage may focus on benefits of the behavior and strategies for maintaining or even increasing the current intake. Likewise, individuals who mistakenly consider themselves to be in maintenance or action could be given nutrition education that makes them aware of their misconception and encourages them to make changes.
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References |
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Received on January 9, 2001; accepted on October 19, 2001
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