Stages of change for fruit and vegetable intake among New Zealand men: readiness to eat five servings a day and impact of contextual factors.
Behavior modification (Reports)
|Publication:||Name: International Journal of Men's Health Publisher: Men's Studies Press Audience: Academic; Professional Format: Magazine/Journal Subject: Health Copyright: COPYRIGHT 2010 Men's Studies Press ISSN: 1532-6306|
|Issue:||Date: Fall, 2010 Source Volume: 9 Source Issue: 3|
|Topic:||Canadian Subject Form: Behaviour modification|
|Product:||Product Code: 0160000 Vegetables NAICS Code: 11121 Vegetable and Melon Farming SIC Code: 0161 Vegetables and melons|
|Organization:||Organization: American Dietetic Association|
|Geographic:||Geographic Scope: New Zealand Geographic Code: 8NEWZ New Zealand|
Successful behavior change according to the transtheoretical model
involves progressing through a series of stages of change. This paper
examines the proportion of men in each stage of change for fruit and
vegetable intake, compares stage classification with intentions and
behavior, and the impact of contextual factors on stage membership. Men
randomly selected from the New Zealand electoral roll completed a postal
questionnaire (n = 518; 45% response rate). One-third (32%) of
respondents were not intending to eat five or more servings of fruit and
vegetables a day (precontemplation stage), 10% were contemplating change
(contemplation stage), 7% preparing to change (preparation stage), and
51% were already doing so in the combined action/maintenance stage.
Intentions increased on average across stages, and fruit and vegetable
intake was at least two servings higher in action/maintenance. Dietary
guideline knowledge, older age, higher income, education and food
security increased the likelihood of being in action/maintenance. Simple
health promotion messages should be directed toward men, promoting
conventional and affordable produce, and dietary guidelines. Community
and policy approaches that increase food skills and knowledge, fruit and
vegetable access and affordability would support behavior change.
Keywords: fruit and vegetable, stages of change, transtheoretical model
Fruit and vegetable (F&V) intake is one factor amenable to change to reduce the risk of chronic health problems including heart disease, stroke, and some cancers (World Cancer Research Fund/American Institute for Cancer Research, 2007; World Health Organization, 2003). High F&V consumption may also protect against chronic disease risk factors including diabetes and hypertension, and lower the risk of obesity when combined with physical activity (Nanney, Haire-Joshu, Hessler & Brownson, 2004; Roberts & Barnard, 2005; Sullivan, Oakden, Young, Lau & Lawson, 2004; Van Duyn & Pivonka, 2000). Similar to World Health Organization (2003) recommendations, New Zealand (N.Z.) food and nutrition guidelines for healthy adults advise eating a variety of nutritious foods including at least five servings of F&Vs a day (Ministry of Health, 2003). A large proportion of adults do not eat the minimum daily recommended amount of F&Vs and the risk is higher among men (Blanck, Gillespie, Kimmons, Seymour & Serdula, 2008; Ministry of Health, 2006, 2008; Serdula, Gillespie, Kettel-Khan, Farris, Seymour & Denny, 2004; Tobias, Jackson, Yeh & Huang, 2007).
Despite the health benefits conferred by eating plenty of F&Vs, the modification of lifestyle behaviors can be complex and challenging, and people differ in their readiness to change. The transtheoretical model (TTM; also known as the stages of change model) is a prominent model of health behavior. The model has been applied to a wide range of health behaviors, has apparent face validity, and has generated optimism about behavior change (Bunton, Baldwin, Flynn & Whitelaw, 2000; Kristal, Glanz, Curry & Patterson, 1999). The TTM conceptualises behavior change as a process which takes place over time through a series of five discrete stages of change (Prochaska & DiClemente, 1992). The stages include precontemplation, contemplation, preparation, action and maintenance. According to the TTM (e.g., Greene, Rossi, Rossi, Velicer, Fava & Prochaska, 1999; Prochaska, Redding& Evers, 2002), those in the precontemplation stage have no intention of changing their behavior within the next six months. In the contemplation stage people are thinking about changing their behavior within the next six months, while those in the preparation stage are planning on changing within the next month. The target behavior is successfully being performed in the action and maintenance stages, which are usually differentiated by a six month time period.
The stages of change reflect a sequence that one moves through in order to achieve behavior change. While regression to an earlier stage can occur, stage models assume movement occurs from one stage to the next (Weinstein, Rothman & Sutton, 1998). The most stable stages are precontemplation and maintenance, in which people may remain for long periods of time (Velicer, Rossi, Prochaska & DiClemente, 1996). The contemplation, preparation and action stages are more dynamic and only a small proportion of people are expected to take meaningful action at any one given time (Greene et al., 1999; Nigg et al., 1999). Those factors which facilitate progression through stages are expected to differ depending on the stage one is in. Interventions matched to stages are therefore expected to be more effective in eliciting behavior change than standardised intervention programmes.
Identification of the stage distribution in the population could aid the design of health promotion programmes aimed at increasing F&V intake. Studies examining stage distributions for dietary behaviors have been reviewed by Horwath (1999) and Spencer and colleagues (2007). Stage distributions found for dietary behaviors depend on methods used to classify people into one of the mutually exclusive and discrete stages. While most measures include a short series of branching yes/no type questions examining current behavior and intentions (Rosen, 2000), the specificity of the target behavior differs (see Horwath, 1999). Studies which have assessed stages using subjective ratings based of the direction of F&V intake (e.g., low or high) result in a large proportion of respondents in the postaction stages of action and maintenance (e.g., Lechner, Brug, De Vries, van Assema & Mudde, 1998; Van Duyn et al., 1998), compared to those using specific action criteria such as five servings of F&Vs a day (e.g., De Vet, De Nooijer, De Vries & Brug, 2005; Laforge, Greene & Prochaska, 1994; Ma, Betts, Horacek, Georgiou, White & Nitzke, 2002).
Accurate stage assignment is a requisite for the delivery of appropriate stage matched interventions. Previous research has called for greater attention to the psychometric properties of stage measures (e.g., Bridle et al., 2005; Brug, Conner, Harre, Kremers, McKellar & Whitelaw, 2005; Bunton et al., 2000). Evidence suggests intentions increase on average across stages for F&V intake when stage assessment includes a specific action criterion (e.g., Armitage, Povey & Arden, 2003). F&V intake is also higher on average in the postaction stages, and a greater proportion of participants are classified correctly based on cut-off values, when assessment includes a specific action criterion (e.g., Campbell et al., 1998; De Vet et al., 2005; Ling & Horwath, 2000; Ma, Betts, Horacek, Georgiou & White, 2003). The use of a clearly specified action criterion that is agreed upon by researchers and professionals in the field, and associated with a lower risk of disease has therefore been recommended (Horwath, 1999; Prochaska et al., 2002; Prochaska & Velicer, 1997).
Factors which aid progression from one stage to the next in order to achieve behavior change differ depending on the stage one is in. That is, different factors are important in different stages of change (Weinstein & Sandman, 2002). Although similar factors are expected to be important among those in the same stage (Weinstein et al., 1998; Weinstein & Sandman, 2002). To develop effective stage matched interventions a clear understanding of factors important in each stage is required (Bridle et al., 2005; Horwath, 1999; Weinstein et al., 1998). Psychosocial factors amenable to change are the primary focus of health behavior theories including the TTM. Contextual factors may also impact on behavior directly or indirectly through psychosocial variables. An examination of sociodemographic and background factors would provide a more comprehensive understanding of behavior change and help identify other avenues for intervention.
Previous research indicates males are more likely to be in the precontemplation stage for F&V intake, while the chances of being in the postaction stages are higher for females (Campbell et al., 1998; De Vet et al., 2005; Ling & Horwath, 1999; Resnicow, McCarty & Baranowski, 2003; Van Duyn et al., 1998). Older age, higher education and income also increase the likelihood of being in the maintenance stage, while awareness of F&V guidelines tends to increase across stages (Campbell et al., 1998; De Vet et al., 2005; Kloek, van Lenthe, van Nierop & Mackenbach, 2004; Resnicow et al., 2003; Van Duyn et al., 1998).
Food insecurity may be important and refers to the limited or uncertain availability of nutritious, safe and personally acceptable foods that can be acquired in socially acceptable ways (Anderson, 1990). Food insecurity ranges in degree from food security (access to nutrition and safe foods at all times), to worry and uncertainty over the household food supply, to reduced variety and quality of foods eaten, to food insufficiency. Although food insecurity and insufficiency are associated with lower F&V consumption (Dixon, Winkleby & Radimer, 2001; Kendall, Olson & Frongillo, 1996; Quine & Morrell, 2006; Radimer, Allsopp, Harvey, Firman & Watson, 1997), there is a paucity of research examining these factors across stages. In one N.Z. study of Maori women (Tassell & Flett, 2005) food insecurity increased on average between the combined precontemplation/contemplation and preparation/action stages, then declined in the maintenance stage for F&V and dietary fat intake.
This study will examine the stage distribution for F&V intake among N.Z. men. An action criterion of five or more servings of F&Vs a day will be used to assess stages and the validity of the measure will be examined in relation to intentions and behavior. Intentions and behavior are expected to be highest in the postaction stages. The impact of contextual factors on stage distributions will also be examined. Significant differences are expected to be found across stages in age, education, income, awareness of F&V guidelines and food insecurity.
A sample of 1200 men aged 18 years and over was randomly selected from the N.Z. electoral roll and invited to participate in the cross-sectional mail survey. In total, 59 men could not be contacted due to incorrect address details or illness. Of the remaining 1,141 men, 518 completed the questionnaire (45% response rate).
Data was collected using a self-administered questionnaire during September and October 2006. A three stage Dillman (2000) method was used that involved mailing out a pre-letter, followed by the questionnaire, freepost reply envelope, and information sheet ten days later. A thank you/reminder postcard was posted two weeks later. The study was conducted in accordance with Massey University ethical guidelines. Consent to take part in the study was implied by completion and return of the survey questionnaire.
Stage of change was assessed with a single item adapted from other stage measures of fruit, high fat foods and health risk behaviors (DeVet et al., 2005; Greene & Rossi, 1998; Nigg et al., 1999). The time frames used were based on TTM stage definitions (Prochaska & DiClemente, 1992). Respondents selected the most appropriate statement for themselves in response to the question "Do you consistently eat at least 5 servings of fruit and vegetables each day?". Participants were classified in precontemplation (no, and I do not intend to change within the next 6 months), contemplation (no, but I intend to change this within the next 6 months), preparation (no, but 1 intend to change within the next month), action (yes, and I started doing this less than 6 months ago), or maintenance (yes, and I have done so for more than 6 months). Examples of F&V servings were outlined to participants prior to stage being assessed.
Usual F&V intake was assessed with a brief food frequency questionnaire (FFQ) containing six items based on those used in U.S. surveys (Block, Gillespie, Rosenbaum & Jenson, 2000; Serdula et al., 2004; Stables et al., 2002). Participants were asked how often over the last month they had consumed fruit juice, fruit (fresh, frozen, canned or stewed), salad, and other cooked vegetables (including those in mixed dishes), potatoes, and hot potato chips (including kumara chips, french fries and wedges) (see Kristal, Glanz, Tilley & Li, 2000; Thompson et al., 2000). Response options ranged from rarely or never to 4+ per day. Based on 5 A Day and N.Z. food and nutrition guidelines, total intake excluded hot potato chips and included a maximum of one serving of juice (Ministry of Health, 2003; Serdula et al., 2004). Similar measures have correlations in the range of .50 and .70 with longer FFQs, diet records and recalls (Block et al., 2000; Campbell et al., 1999; Kristal et al., 2000; Serdula et al., 1993), and have been associated with biomarkers of intake (Coyne et al., 2004; Resnicow et al., 2000).
Intentions were assessed with a single item based on previous research (Armitage et al., 2003). Participants rated the extent to which they agreed with the statement "I intend to eat 5 or more servings of fruit and vegetables each day in the future" on a 7-point scale ranging from 1 (strongly disagree) to 7 (strongly agree).
Household food insecurity was assessed with eight items used in N.Z. nutrition surveys (Parnell, Reid, Wilson, McKenzie & Russell, 2001; Parnell, Scragg, Wilson, Schaaf & Fitzgerald, 2003). For example, "food runs out in our household due to a lack of money." Items were rated on a 7-point scale ranging from 1 (never) to 7 (all of the time). One item "we can afford to eat properly" was reverse scored. Participants who seldom or never experienced food insecurity were classified as food secure. Remaining participants who experienced one or more aspects of food insecurity at least once in awhile were coded food insecure.
Awareness of F&V guidelines were assessed separately and based on items used in previous research (Havas, Anliker, Damron, Lagenberg, Ballesteros & Feldman, 1998; Steptoe, Perkins-Porras, McKay, Rink, Hilton & Cappuccio, 2003). Participants were asked "how many servings of fruit (vegetables) do doctors recommend each day?". Response options ranged from I or fewer to 5 or more. In line with guidelines promoted by the Ministry of Health (2003) and 5 A Day programme (Ashfield-Watt, Stewart & Scheffer, 2004), participants were categorised as aware if the total number was at least five.
Sociodemographic data was collected using questions modelled on the 2006 N.Z. Census (Statistics N.Z., 2006) and included age, personal income, education and ethnicity.
Data was screened prior to analysis and four cases with more than 25% of missing data were removed from the study. Pairwise deletion was used for remaining cases with missing data. Fifteen men were in the action stage but as the power to detect significant mean differences is influenced by sample size, the action and maintenance stages were combined (see Cohen, 1992). Chi-square tests were used to analyse differences in categorical variables across the stages of change, and one way analysis of variance with Games Howell post-hoc tests for continuous variables.
The magnitude of standardised mean differences was assessed with Cohen's d, using the standard deviation of the two groups being compared. Based on Cohen's (1992) guidelines, standardised mean differences of .20, .50, and .80 reflect small, medium and large effects respectively. Eta squared ([[eta].sup.2]) was used to determine the total proportion of variance accounted for in the dependent variable due to differences in group means. For measures of association, Cohen's (1992) guidelines suggest 1%, 9% and 25% represent small, medium and large effects respectively. All statistical analyses were performed using SPSS version 14.0.
Participant characteristics are summarised in Table 1. The stage distribution or proportion of men in each stage of change for F&V intake is presented in Table 2. One-third (32%) of men in the precontemplation stage were not intending to eat five or more servings of F&Vs a day in the future, 10% were contemplating change, 7% were preparing to change within the next month, and 51% reported already eating at least five servings daily in the action/maintenance stage.
Mean F&V intake assessed with the brief FFQ differed significantly across the stages of change F(3,505) = 52.04, p < .001, [[eta].sup.2] =.24). As expected, F&V consumption was on average significantly higher in action/maintenance (M = 5.08, SD = 2.21 ) compared to the precontemplation (M = 3.08, SD = 1.60, d = 1.04), contemplation (M = 2.70, SD = 1.70, d = 1.21), and preparation stages (M = 2.80, SD = 1.34, d = 1.25). Men in action/maintenance consumed 2.00 to 2.38 more servings of F&Vs a day on average. F&V intake did not differ between the preaction stages.
Intentions differed significantly F(3,504) = 97.66, p < .001, [[eta].sup.2] =.37) and increased on average across stages. Significant differences in intentions were found between each stage, except action/maintenance and preparation, which was approaching significance (p = .06). Between precontemplation (M = 3.39, SD = 1.64) and contemplation (M = 4.59, SD = 1.43) intentions increased by .78 standard deviations, and by about half a standard deviation between contemplation and preparation (M = 5.32, SD = 1.23, d = 0.55), and preparation and action/maintenance (M= 5.88, SD = 1.36, d = 0.43). As expected, intentions were highest on average in action/maintenance.
Age, education, income, food insecurity, and awareness of dietary guidelines differed significantly across stages as expected (see Table 2).
The study investigated the proportion of respondents in each stage of change for F&V intake, level of intentions and behavior in each stage, and impact of contextual factors on stage membership, in a random sample of N.Z. men.
A parsimonious stage measure adapted for F&V intake was used to classify men into one of the stages of change for eating at least five servings of F&Vs daily. Intentions increased on average across stages in line with previous research (Armitage et al., 2003). While F&V intake did not differ across the preaction stages of precontemplation, contemplation and preparation, a large increase was found in the postaction stages. On average, men in action/maintenance were eating five servings of F&Vs a day in line with dietary guidelines. The pattern of F&V intake corroborates with earlier studies that assessed behavior with brief FFQs (e.g., Campbell et al., 1998; Greene, Fey-Yensan, Padula, Rossi, Rossi & Clark, 2004). Although research using other dietary assessment methods (e.g., 24 hour recalls, quantitative FFQs) have found small to moderate increases in F&V intake across the preaction stages, moderate to large increases in consumption have been found in the postaction stages (e.g., De Vet et al., 2005; Greene et al., 2004; Ling & Horwath, 2000; Ma et al., 2003). Findings provide some evidence to support the validity of the stage measure used.
Half the men surveyed were in the action/maintenance stage for F&V intake. The direct comparison of findings with earlier studies is limited by the range of stage measures used. Research in the U.K., U.S., and the Netherlands have found slightly fewer participants in the postaction stages for F&V intake using similar action criteria (e.g., Armitage et al., 2003; De Vet et al., 2005; Ma et al., 2002). While secular trends may partly explain these findings, international comparisons suggest F&V intake may be slightly higher in N.Z. (Ministry of Health & University of Auckland, 2003). The slightly older age of participants compared to the general population also likely influenced results given the positive relationship between age and F&V intake (e.g., Guenther, Dodd, Reedy & Krebs-Smith, 2006; Ministry of Health, 2008). A wide variety of fresh produce was also available when the study was undertaken. While the proportion of men in the postaction stages is in line with N.Z. surveys (Tobias et al., 2007) nearly half of men may benefit from higher F&V consumption.
Men with low F&V intake differed in their readiness to change. In total, one-third of men were in the precontemplation stage and had no intentions of changing their behavior. Although precontemplators are often considered more resistant to change, on average they only mildly disagreed with the statement they intended to eat five or more servings of F&Vs a day in the future. One in six men were either in the contemplation or preparation stages and thinking about modifying their behavior. On average, research using similar action criteria for F&V intake has found fewer respondents in precontemplation and slightly more in the contemplation and preparation stages (Armitage et al., 2003; De Vet et al., 2005; Laforge et al., 1994; Ma et al., 2003; Sullivan et al., 2004). The variation is attributed to gender differences. Finding the largest proportion of men with low F&V intake were in the precontemplation stage is in line with previous research and TTM predictions. To improve dietary behavior interventions are required that target men with low intake (particularly precontemplators) and encourage them to start thinking about eating more F&Vs.
The proportion of men in each stage for F&V intake differed in relation to income, education, age, food security and dietary guideline knowledge. Men who were older, who had a higher level of education, income, food security and awareness of dietary guidelines were more likely to be in the action/maintenance stage. The stage distributions found for contextual variables corroborate with earlier studies (Campbell et al., 1998; De Vet et al., 2005; Kloek et al., 2004; Resnicow et al., 2003; Tassell & Flett, 2005; Van Duyn et al., 1998). The 5+ A Day study (Van Duyn et al., 1998) for example found those in the maintenance stage for F&V intake tended to have a higher level of education and income. On average, N.Z. Maori women in the maintenance stage for F&V and dietary fat intake were also more food secure (Tassell & Flett, 2005).
Results have practical implications for nutrition education and messages aimed at promoting F&V intake. Dietary guidelines should continue to be promoted given more than three-quarters of men who were unaware of F&V recommendations had a low intake. The 5 A Day program has increased awareness of F&V guidelines (Ashfield-Watt et al., 2004; Stables et al., 2002). Avenues used to promote F&V guidelines however require further consideration to reach men. Nutrition and health information is often directed towards women (Courtenay, 2000; Lyons & Willott, 1999), despite men considerably influencing household food choices (Brown & Miller, 2002; Jensen & Holm, 1999). U.S. surveys suggest both men and women frequently use television, magazines and newspapers as sources of nutrition information (American Dietetic Association, 2002). While the internet offers new avenues for health promotion, internet access is not universal and less common among those who are older and from lower socioeconomic backgrounds (Couper, 2000; Dillman, 2000; Statistics N.Z., 2008; Tu & Hargraves, 2003). Continued promotion of F&V guidelines is recommended using appropriate communication channels to reach men.
To improve dietary behavior health promotion messages need to be simple and emphasise F&Vs commonly eaten. Half of men with low education were in the pre-contemplation or contemplation stages. Conversely, two-thirds of men with high education were in the preparation or action/maintenance stages. Education may influence access and understanding of nutrition and health-related information (Turrell & Kavanagh, 2006; Worsley, Blasche, Ball & Crawford, 2003). Evidence suggests education is related to dietary knowledge (Axelson, Federline & Brinberg, 1986). A lower level of education is also associated with preferences for more traditional and familiar foods (Steptoe & Wardle, 1999). Messages aimed at improving dietary behavior therefore need to be simple, emphasise conventional F&Vs and effectively disseminated to reach target audiences.
Household food insecurity needs improving if men thinking about eating a healthy diet are to translate their intentions into action. Men contemplating or preparing to change their dietary behavior were more likely to experience household food insecurity. A recent review found food insecurity was most frequently associated with household income (Gorton, Bullen & Ni Mhurchu, 2010). Other food insecurity risk factors include younger age, lower socioeconomic status, larger household size and having dependent children (Parnell et al., 2001; 2003; Nord, Andrews & Carlson, 2007). These risk factors may influence food insecurity in part by impacting on the level of household disposable income. Achieving successful behavior change may be more challenging for food insecure households.
Several reports summarise initiatives aimed at improving food security, including food access, skills and knowledge (Te Hotu Manawa Maori, 2007; NSW Centre for Public Health Nutrition, 2003). Interventions include advice on purchasing and the preparation of healthy meals within a limited budget, cooking demonstrations and supermarket tours. Practical means of storing and keeping F&Vs fresh for longer may also be beneficial. A recent review (Gorton et al., 2010) on food insecurity has also called for community and policy approaches addressing multiple areas to improve food insecurity, such as labour market, family support and welfare policies. Such strategies may contribute to improving F&V access and affordability.
Men who were eating sufficient servings of F&Vs daily in the current study were more food secure and had greater resources enabling them to do so including higher income. Food cost is perceived as a greater barrier to the consumption of a healthy diet in low income groups (Eikenberry & Smith, 2004; Giskes, Turrell, Patterson & Newman, 2002; Pollard, Kirk & Cade, 2002; Turrell & Kavanagh, 2006). Food pricing currently favours energy dense, nutrient poor foods high in grains, sugars and fats rather than healthier food choices (Drewnowski & Darmon, 2005; Rush, Puniani, Snowling & Paterson, 2007). A review of randomised controlled trials suggests monetary incentives may positively influence dietary behavior (Wall, Ni Mhurchu, Blakely, Rodgers & Wilton, 2006). Fiscal approaches may include the use of coupons or vouchers for high risk groups, discounting or reduced taxes on healthy food items, increased taxes on energy dense foods, and food industry incentives for the production and marketing of healthy food items (Glanz & Hoelscher, 2004; Ni Mhurchu & Ogra, 2007; Popkin, Duffey & Gordon-Larsen, 2005; Wall et al., 2006). In Australia healthy food is exempt from goods and services tax (Australian Taxation Office, 2006) and evidence suggests taxing energy dense foods such as fast foods, could be effective for improving overall diet quality (Beydoun, Powell & Wang, 2008).
Future research should consider the pathways by which contextual variables impact on behavior. Local access and availability of F&Vs may be important. Although access to supermarkets and convenience stores selling healthy food items is better in N.Z. in more deprived areas (Pearce, Blakely, Witten & Bartie, 2007; Pearce, Witten, Hiscock & Blakely, 2007), barriers may include transport, the quality of produce available, and storage facilities (e.g., Giskes et al., 2002; Reicks, Randall & Haynes, 1994). While the current study design precluded an examination of cultural factors, these should be examined in future research given their impact on food acceptability, taboos and preferences, along with the value and meaning of different foods, and health beliefs (Crotty, 2001; Fieldhouse, 1995; Rozin, 1996).
Several limitations need to be taken into account when interpreting results. Although the action and maintenance stages have been combined in previous research (Lechner et al., 1998; Ling & Horwath, 1999), this strategy limits an examination of factors discriminating between these stages. F&V intake was also assessed with a brief FFQ given its relative ease, suitability for self-administration, and low respondent burden. Although earlier studies (Campbell et al., 1998; De Vet et al., 2005; Greene et al., 2004; Ma et al., 2003) have used FFQs, more comprehensive dietary assessment methods are available, such as multiple diet records and recalls. Such estimates may be used in future research to assess predictive values or the proportion of participants classified correctly in each stage based on cut-off values (Ling & Horwath, 2000).
The study response rate was in line with expectations based on earlier dietary surveys. It is likely participants were however more health conscious and interested in dietary behavior than non-respondents. The proportion of men in the preaction stages may be underestimated. Men who were younger and from lower socioeconomic backgrounds were also underrepresented, and likely influenced rates of food insecurity and knowledge of dietary guidelines. Household rather than personal income may have been more important to consider given income is often shared among household members (O'Dea & Howden-Chapman, 2000). As the majority of participants were N.Z. Europeans, the impact of ethnicity on stage classification was not examined and limits the generalisability of findings. Given the study also used a cross-sectional research design, causation cannot be assumed.
Finally, finding intentions and behavior differed across the stages of change does not necessarily support the assumption that behavior change follows a stage process (see Horwath, 1999). Several reviews have questioned whether the stages, or subsets of them, reflect genuine stages or an underlying continuum based on intentions and behavior (Bandura, 2000; Littell & Girvin, 2002; West, 2005). The pattern of scores on intentions and behavior suggests the stages do not reflect a single underlying continuum (see Sutton, 2000; Weinstein et al., 1998). It is however possible the preaction stages reflect a continuum based on intentions. In which case, similar factors may facilitate progression in the preaction stages. Rather than progressing through stages, behavior change may also involve small naturally occurring shifts along a continuum (Weinstein et al., 1998). The use of longitudinal and experimental research designs in future research would enable stronger tests of these stage model assumptions.
In conclusion, the stages of change were used to describe men's readiness to eat five or more servings of F&Vs a day. Half the men had low F&V intake. Given F&V intake is one factor amenable to change to protect against chronic health problems and reduce the burden of future disease, a large number of men may benefit from greater consumption. Although the proportion of men in the precontemplation stage presents a challenge to health promoters, the characteristics of men in each stage could aid in targeting interventions. Moreover, interventions which reach and have a positive impact on men in the preaction stages could help reduce socioeconomic inequalities in health attributed to dietary factors. Positive shifts in dietary behavior may be brought about by multiple-level and comprehensive approaches which combine individual, community and policy interventions and strategies.
Recommendations for promoting F&V intake:
* direct health promotion messages towards men
* use appropriate communication channels
* use simple key messages
* emphasise affordable and inseason F&Vs
* promote traditional and commonly eaten F&Vs
* increase dietary guideline knowledge
American Dietetic Association. (2002). Nutrition & you: Trends 2002. Chicago: American Dietetic Association.
Anderson, S.A. (1990). Core indicators of nutritional state for difficult-to-sample populations. Journal of Nutrition, 120(11), 1559-1600.
Armitage, C.J., Povey, R., & Arden, M.A. (2003). Evidence for discontinuity patterns across the stages of change: A role for attitudinal ambivalence. Psychology and Health, 18(3), 373-386.
Ashfield-Watt, P.A.L., Stewart, E., & Scheffer, J. (2004). 5+ a day: are we getting the message across? Asia Pacific Journal of Clinical Nutrition, 13(Suppl.), S38.
Australian Taxation Office. (2006). GST and food-schedules I and 2. Canberra, Australia: Australian Taxation Office.
Axelson, M.L., Federline, T.L., & Brinberg, D. (1985). A meta-analysis of food- and nutrition-related research. Journal of Nutrition Education, 17(2), 51-54.
Bandura, A. (2000). Health promotion from the perspective of social cognitive theory. In P. Norman, C. Abraham, & M. Conner (Eds.), Understanding and changing health behavior from health beliefs to self-regulation (pp. 299-342). The Netherlands: Harwood Academic.
Beydoun, M.A., Powell, L.M., & Wang, Y. (2008). The association of fast food, fruit and vegetable prices with dietary intakes among US adults: is there modification by family income? Social Science & Medicine, 66, 2218-2229.
Blanck, H.M., Gillespie, C., Kimmons, J.E., Seymour, J.D., & Serdula, M.K. (2008). Trends in fruit and vegetable consumption among U.S. men and women, 1994-2005. Preventing Chronic Disease, Public Health Research, Practice & Policy, 5(2), 1-10.
Block, G., Gillespie, C., Rosenbaum, E.H., & Jenson, C. (2000). A rapid food screener to assess fat and fruit and vegetable intake. American Journal of Preventive Medicine, 18(4), 284-288.
Bridle, C., Riemsma, R.P., Pattenden, J., Sowden, A.J., Mather, L., Watt, I.S., et al. (2005). Systematic review of the effectiveness of health behavior interventions based on the transtheoretical model. Psychology and Health, 20(3), 283-301.
Brown, J.L., & Miller, D. (2002). Couples' gender role preferences and management of family food preferences. Journal of Nutrition Education & Behavior, 34, 215-223.
Brug, J., Conner, M., Harre, N., Kremers, S., McKellar, S., & Whitelaw, S. (2005). The Transtheoretical Model and stages of change: A critique. Observations by five commentators on the paper by Adams, J. and White, M. (2004). Why don't stage-based activity promotion interventions work? Health Education Research, 20(2), 244-258.
Bunton, R., Baldwin, S., Flynn, D., & Whitelaw, S. (2000). The 'stages of change' model in health promotion: Science and ideology. Critical Public Health, 10(1), 55-70.
Campbell, M.K., Demark-Wahnefried, W., Symons, M., Kalsbeek, W.D., Dodds, J., Cowan, A., et al. (1999). Fruit and vegetable consumption and prevention of cancer: The black churches united for better health project. American Journal of Public Health, 89(9), 1390-1396.
Campbell, M.K., Symons, M., Demark-Wahnefried, W., Polhamus, B., Bernhardt, J.M., McClelland, J.W., et al. (1998). Stages of change and psychosocial correlates of fruit and vegetables consumption among rural African-American church members. American Journal of Health Promotion, 12(3), 185-191.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155-159.
Couper, M.P. (2000). Web surveys: a review of issues and approaches. Public Opinion Quarterly, 64, 464-494.
Courtenay, W.H. (2000). Engendering health: A social constructionist examination of men's health beliefs and behaviors. Psychology of Men and Masculinity, 1 (1), 4-15.
Coyne, T., Ibiebele, T., McNaughton, S., Rutishauser, I.H.E., O'Dea, K., Hodge, A.M., et al. (2004). Evaluation of brief dietary questions to estimate vegetable and fruit consumption--using serum carotenoids and red-cell folate. Public Health Nutrition, 8(3), 298-308.
Crotty, P. (2001). Food and class. In J. Germov & L. Williams (Eds.). A sociology of food and nutrition: The social appetite (pp. 135-148). Victoria, Australia: Oxford University Press.
De Vet, E., De Nooijer, J., De Vries, N.K., & Brug, J. (2005). Stages of change in fruit intake: A longitudinal examination of stability, stage transitions and transition profiles. Psychology and Health, 20(4), 415-428.
Dillman, D.A. (2000). Mail and internet surveys: The tailored design method (2nd ed.). New York: John Wiley.
Dixon, L.B., Winkleby, M.A., & Radimer, K.L. (2001). Dietary intakes and serum nutrients differ between adults from food-insufficient and food-sufficient families: Third national health and nutrition examination survey, 1988-1994. Journal of Nutrition, 131, 1232-1246.
Drewnowski, A., & Darmon, N. (2005). Food choices and diet costs: An economic analysis. Journal of Nutrition, 135, 900-904.
Eikenberry, N., & Smith, C. (2004). Healthful eating: perceptions, motivations, barriers, and promoters in low-income Minnesota communities. Journal of the American Dietetic Association, 104, 1158-1161.
Fieldhouse, P. (1995). Food and nutrition: Customs and culture (2nd ed.). London: Chapman & Hall.
Giskes, K., Turrell, G., Patterson, C., & Newman, B. (2002). Socio-economic differences in fruit and vegetable consumption among Australian adolescents and adults. Public Health Nutrition, 5(5), 663-669.
Glanz, K., & Hoelscher, D. (2004). Increasing fruit and vegetable intake by changing environments, policy and pricing: Restaurant-based research, strategies, and recommendations. Preventive Medicine, 39, S88-S93.
Gorton, D., Bullen, C.R., & Ni Mhurchu, C. (2010). Environmental influences on food security in high-income countries. Nutrition Reviews, 68(1), 1-29.
Greene, G.W., & Rossi, S.R. (1998). Stages of change for reducing dietary fat intake over 18 months. Journal of the American Dietetic Association, 98(5), 529-534.
Greene, G.W., Fey-Yensan, N., Padula, C., Rossi, S., Rossi, J., & Clark, P.G. (2004). Differences in psychosocial variables by stage of change for fruits and vegetables in older adults. Journal of the American Dietetic Association, 104, 1236-1243.
Greene, G.W., Rossi, S.R., Rossi, J.S., Velicer, W.F., Fava, J.L., & Prochaska, J.O. (1999). Dietary applications of the stages of change model. Journal of the American Dietetic Association, 99(6), 673-678.
Guenther, P.M., Dodd, K.W., Reedy, J., & Krebs-Smith, S .M. (2006). Most Americans eat much less than recommended amounts of fruits and vegetables. Journal of the American Dietetic Association, 106, 1371-1379.
Havas, S., Anliker, J., Damron, D., Lagenberg, P., Ballesteros, M., & Feldman, R. (1998). Final results of the Maryland WIC 5-a-day promotion program. American Journal of Public Health, 88(8), 1161-1167.
Horwath, C.C. (1999). Applying the transtheoretical model to eating behavior change: challenges and opportunities. Nutrition Research Reviews, 12, 281-317.
Jensen, K.O., & Holm, L. (1999). Preferences, quantities and concerns: Socio-cultural perspectives on the gendered consumption of foods. European Journal of Clinical Nutrition, 53, 351-359.
Kendall, A., Olson, C.M., & Frongillo, E.A. (1996). Relationship of hunger and food insecurity to food availability and consumption. Journal of the American Dietetic Association, 96, 1019-1024.
Kloek, G.C., van Lenthe, F.J., van Nierop, P.W.M. & Mackenbach, J.P. (2004). Stages of change for fruit and vegetable consumption in deprived neighborhoods. Health Education & Behavior, 31(2), 223-241.
Kristal, A.R., Glanz, K., Curry, S.J., & Patterson, R.E. (1999). How can stages of change be best used in dietary interventions? Journal of the American Dietetic Association, 99(6), 679-684.
Kristal, A.R., Glanz, K., Tilley, B.C., & Li, S. (2000). Mediating factors in dietary change: Understanding the impact of a worksite nutrition intervention. Health Education & Behavior, 27, 112-125.
Laforge, R.G., Greene, G.W., & Prochaska, J.O. (1994). Psychosocial factors influencing low fruit and vegetable consumption. Journal of Behavioral Medicine, 17(4), 361-374.
Lechner, L., Brug, J., De Vries, H., van Assema, P., & Mudde, A. (1998). Stages of change for fruit, vegetable and fruit intake: Consequences and misconception. Health Education Research, 13(1), 1-11.
Ling, A.M.C., & Horwath, C. (1999). Self-efficacy and consumption of fruit and vegetables: Validation of a summated scale. American Journal of Health Promotion, 13(5), 290-298.
Ling, A.M.C., & Horwath, C. (2000). Defining and measuring stages of change for dietary behaviors: Readiness to meet fruit, vegetable, and grain guidelines among Chinese Singaporeans. Journal of the American Dietetic Association, 100(8), 898-904.
Littell, J.H., & Girvin, H. (2002). Stages of change: A critique. Behavior Modification, 26(2), 223-273.
Lyons, A., & Willott, S. (1999). From suet pudding to superhero: Representations of men's health for women. Health, 3(3), 283-302.
Ma, J., Betts, N.M., Horacek, T., Georgiou, C., & White, A. (2003). Assessing stages of change for fruit and vegetable intake in young adults: A combination of traditional staging algorithms and food-frequency questionnaires. Health Education Research, 18(2), 224-236.
Ma, J., Betts, N.M., Horacek, T., Georgiou, C., White, A., & Nitzke, S. (2002). The importance of decisional balance and self-efficacy in relation to stages of change for fruit and vegetable intakes by young adults. American Journal of Health Promotion, 16(3), 157-166.
Ministry of Health & University of Auckland. (2003). Nutrition and the burden of disease: New Zealand 1997-2011. Wellington, New Zealand: Ministry of Health.
Ministry of Health. (2003). Food and nutrition guidelines of healthy adults: A background paper. Wellington, New Zealand; Ministry of Health.
Ministry of Health. (2006). A comparison of selected findings from the 1996/97 and 2002/03 New Zealand health surveys. Wellington: Ministry of Health.
Ministry of Health. (2008). A portrait of health: key results of the 2006/07 New Zealand health survey. Wellington, New Zealand: Ministry of Health.
Nanney, M.S., Haire-Joshu, D., Hessler, K., & Brownson, R.C. (2004). Rationale for a consistent "powerhouse" approach to vegetable and fruit messages. Journal of the American Dietetic Association, 104(3), 352-356.
Ni Mhurchu, C., & Ogra, S. (2007). The price of healthy eating: Cost and nutrient value of selected regular and healthier supermarket foods in New Zealand. New Zealand Medical Journal, 120(1248).
Nigg, C.R., Burbank, P.M., Padula, C., Dufresne, R., Rossi, J.S., Velicer, W.F., et al. (1999). Stages of change across ten health risk behaviors for older adults. Gerontologist, 39(4), 473-482.
Nord, M., Andrews, M., & Carlson, S. (2007). Household food insecurity in the United States, 2006. Washington, DC: United States Department of Agriculture, Economic Research Service.
NSW Centre for Public Health Nutrition. (2003). Food security options paper: A planning framework and menu of options for policy and practice interventions. Sydney, Australia: NSW Centre for Public Health Nutrition.
O'Dea, D., & Howden-Chapman, P. (2000). Income and income inequality and health. In P. Howden-Chapman & M. Tobias (Eds.), Social inequalities in health, New Zealand 1999: A summary (pp. 65-86). Wellington, New Zealand: Ministry of Health.
Parnell, W., Scragg, R., Wilson, N., Schaaf, D., & Fitzgerald, E. (2003). NZ food NZ children: key results of the 2002 national children's nutrition survey. Wellington, New Zealand: Ministry of Health.
Parnell, W.R., Reid, J., Wilson, N.C., McKenzie, J., & Russell, D.G. (2001). Food security: Is New Zealand a land of plenty? New Zealand Medical Journal, 114(1128), 141-145.
Pearce, J., Blakely, T., Witten, K., & Bartie, P. (2007). Neighborhood deprivation and access to fast-food retailing. American Journal of Preventive Medicine, 32(5), 375-382.
Pearce, J., Witten, K., Hiscock, R., & Blakely, T. (2007). Are socially disadvantaged neighbourhoods deprived of health-related community resources? International Journal of Epidemiology, 36, 348-355.
Pollard, J., Kirk, S.F.L., & Cade, J.E. (2002). Factors affecting food choice in relation to fruit and vegetable intake: a review. Nutrition Research Reviews, 15, 373-387.
Popkin, B.M., Duffey, K., & Gordon-Larsen, P. (2005). Environmental influences on food choice: physical activity and energy balance. Physiology & Behavior, 86, 603-613.
Prochaska, J.O., & DiClemente, C.C. (1992). Stages of change in the modification of problem behaviors. Progress in Behavior Modification, 28, 183-218.
Prochaska, J.O., & Velicer, W.F. (1997). Misinterpretations and misapplications of the transtheoretical model. American Journal of Health Promotion, 12(1), 11-12.
Prochaska, J.O., Redding, C.A., & Evers, K.E. (2002). The transtheoretical model and stages of change. In K. Glanz, B.K. Rimer & F.M. Lewis (Eds.), Health behavior and health education: theory, research, and practice (3rd ed., pp. 99-120). San Francisco: Jossey-Bass.
Quine, S., & Morrell, S. (2006). Food insecurity in community-dwelling older Australians. Public Health Nutrition, 9(2), 219-224.
Radimer, K.L., Allsopp, R., Harvey, P.W.J., Firman, D.W., & Watson, E.K. (1997). Food insufficiency in Queensland. Australian and New Zealand Journal of Public Health, 21(3), 303-310.
Reicks, M., Randall, J.L., & Haynes, B. (1994). Factors affecting consumption of fruits and vegetables by low-income families. Journal of the American Dietetic Association, 94(11), 1309-1311.
Resnicow, K., McCarty, F., & Baranowski, T. (2003). Are precontemplators less likely to change their dietary behavior? A prospective analysis. Health Education Research, 18(6), 693-705.
Resnicow, K., Odom, E., Wang, T., Dudley, W.N., Mitchell, D., Vaughan, R., et al. (2000). Validation of three food frequency questionnaires and 24-hour recalls with serum carotenoid levels in a sample of African-American adults. American Journal of Epidemiology, 152(11), 1072-1080.
Roberts, C.K., & Barnard, R.J. (2005). Effects of exercise and diet on chronic disease. Journal of Applied Physiology, 98, 3-30.
Rosen, C.S. (2000). Is the sequencing of change processes by stage consistent across health problems? A meta-analysis. Health Psychology, 19(6), 593-604.
Rozin, P. (1996). Sociocultural influences on human food selection. In E.D. Capaldi (Ed.), Why we eat what we eat: The psychology of eating (pp. 233-263). Washington, DC: American Psychological Association.
Rush, E., Puniani, N., Snowling, N., & Paterson, J. (2007). Food security, selection, and healthy eating in a Pacific community in Auckland New Zealand. Asia Pacific Journal of Clinical Nutrition, 16(3), 448-454.
Serdula, M., Coates, R., Byers, T., Mokdad, A., Jewell, S., Chavez, N., et al. (1993). Evaluation of a brief telephone questionnaire to estimate fruit and vegetable consumption in diverse study populations. Epidemiology, 4, 455-463.
Serdula, M.K., Gillespie, C., Kettel-Khan, L., Farris, R., Seymour, J., & Denny, C. (2004). Trends in fruit and vegetable consumption among adults in the United States: Behavioral Risk Factors Surveillance System, 1994-2000. American Journal of Public Health, 94(6), 1014-1018.
Spencer, L., Wharton, C., Moyle, S., & Adams, T. (2007). The transtheoretical model as applied to dietary behavior and outcomes. Nutrition Research Reviews, 20, 46-73.
Stables, G.J., Subar, A.F., Patterson, B .H., Dodd, K., Heimendinger, J., Van Duyn, M.A.S. et al. (2002). Changes in vegetable and fruit consumption and awareness among US adults: Results of the 1991 and 1997 5 a day for better health program surveys. Journal of the American Dietetic Association, 102, 809-817.
Statistics New Zealand. (2006). 2006 Census. Wellington, New Zealand: Statistics New Zealand.
Statistics New Zealand. (2008). Quickstats. Retrieved September 5, 2008, from http://www.stats. govt.nz/cenus/2006-census-data/QuickStats.htm.
Steptoe, A., & Wardle, J. (1999). Motivational factors as mediators of socioeconomic variations in dietary intake patterns. Psychology and Health, 14, 391-402.
Steptoe, A., Perkins-Porras, L., McKay, C., Rink, E., Hilton, S., & Cappuccio, F.P. (2003). Psychological factors associated with fruit and vegetable intake and with biomarkers in adults from a low-income neighbourhood. Health Psychology, 22(2), 148-155.
Sullivan, C., Oakden, J., Young, J., Lau, M., & Lawson, R. (2004). Pulp fiction--the facts harvested: A study of New Zealander's physical activity and nutrition. Wellington, New Zealand: Cancer Society.
Sutton, S. (2000). Interpreting cross-sectional data on stages of change. Psychology and Health, 15, 163-171.
Tassell, N., & Flett, R. (2005). Stages of change for fruit and vegetable intake and dietary fat modification in Maori women: Some relationships with body attitudes and eating behaviors. New Zealand Journal of Psychology, 34(1), 28-34.
Te Hotu Manawa Maori. (2007). Food security among Maori in Aotearoa. Auckland, New Zealand: Te Hotu Manawa Maori.
Thompson, F.E., Kipnis, V., Subar, A.F., Krebs-Smith, S.M., Kahle, L.L., Midthune, D., et al. (2000). Evaluation of 2 brief instruments and a food-frequency questionnaire to estimate daily number of servings of fruit and vegetables. American Journal of Clinical Nutrition, 71, 1503-1510.
Tobias, M., Jackson, G., Yeh, L-C., & Huang, K. (2007). Do healthy and unhealthy behaviors cluster in New Zealand? Australian and New Zealand Journal of Public Health, 31(2), 155-163.
Tu, H.T., & Hargraves, J.L. (2003). Seeking health care information: Most consumers still on the sidelines. Issue Brief, 61. Retrieved April 8, 2007, from http://www.hschange.org
Turrell, G., & Kavanagh, A.M. (2006). Socio-economic pathways to diet: Modelling the association between socio-economic position and food purchasing behavior. Public Health Nutrition, 9(3), 375-383.
Van Duyn, M.A.S., & Pivonka, E. (2000). Overview of the health benefits of fruit and vegetable consumption for the dietetics professional: Selected literature. Journal of the American Dietetic Association, 100, 1511-1521.
Van Duyn, M.A.S., Heimendinger, J., Russek-Cohen, E., DiClemente, C.C., Sims, L.S., Subar, A.F., et al. (1998). Use of the transtheoretical model of change to successfully predict fruit and vegetable consumption. Journal of Nutrition Education, 30, 371-380.
Velicer, W.F., Rossi, J.S., Prochaska, J.O., & DiClemente, C.C. (1996). A criterion measurement model for health behavior change. Addictive Behaviors, 21(5), 555-584.
Wall, J., Ni Mhurchu, C., Blakely, T., Rodgers, A., & Wilton, J. (2006). Effectiveness of monetary incentives in modifying dietary behavior: A review of randomized, controlled trials. Nutrition Reviews, 64(12), 518-531.
Weinstein, N.D., Rothman, A.J., & Sutton, S.R. (1998). Stage theories of health behavior: Conceptual and methodological issues. Health Psychology, 17(3), 290-299.
Weinstein, N.D., & Sandman, P.M. (2002). Reducing the risks of exposure to radon gas: An application of the precaution adoption process model. In D. Rutter & L. Quine (Eds.), Changing health behaviour (pp. 66-86). Buckingham, UK: Open University Press.
West, R. (2005). Time for a change: Putting the transtheoretical (stages of change) model to rest. Addiction, 100, 1036-1039.
World Cancer Research Fund/American Institute for Cancer Research. (2007). Food, nutrition, physical activity, and the prevention of cancer: A global perspective. Washington, DC: American Institute for Cancer Research.
World Health Organization (WHO). (2003). Diet, nutrition and the prevention of chronic diseases. Report of the joint WHO/FAO expert consultation. Geneva, Switzerland: WHO.
Worsley, A., Blasche, R., Ball, K., & Crawford, D. (2003). The relationship between education and food consumption in the 1995 Australian national nutrition survey. Public Health Nutrition, 7(5), 649-663.
Angela Jury (a) and Ross Flett (a)
(a) School of Psychology, Massey University, New Zealand.
Correspondence concerning this article should be addressed to Dr. Ross Flett, School of Psychology, Massey University, Private Bag 11-222, Palmerston North 4442, New Zealand. Email: firstname.lastname@example.org
Table 1 Descriptive Characteristics of Participants, N = 514 M (SD) n (%) Age (+) 51.20 (16.16) F&V intake ([dagger]) 3.92 (2.08) Ethnicity * Maori 35 (7) Pacific 12 (2) Asian 32 (6) N.Z. European/other 454 (89) Note. (+) range 18-97 years. ([dagger]) Median intake = 3.54. * 22 participants belonged to more than one ethnic group. F&V = fruit and vegetable, N.Z. = New Zealand. Table 2 Descriptive Characteristics of Participants (row percentage) * Full sample Stage of change n % PC C P A/M 509 32 10 7 51 Age 18-44 years 169 33 30 14 15 40 45+ years 339 67 32 9 4 56 Education (+) [pounds sterling] High 194 40 38 11 9 42 school > High school 296 60 27 9 7 57 Income Loss/zero-$20,000 101 20 42 13 7 39 $20,001-$40,000 137 27 31 10 10 49 $40,001-$70,000 157 31 28 13 8 51 $70,001 and over 105 21 26 7 3 65 Household food insecurity Food insecure 161 32 30 16 15 39 Food secure 345 68 32 8 4 56 Aware of F & V guidelines No 115 23 57 14 4 25 Yes 387 77 24 9 8 58 Note. PC = Precontemplation, C = Contemplation, P = Preparation, A/M = Action/maintenance. F&V = fruit and vegetable. (+) The highest level of education could not be determined for 14 participants with qualifications obtained overseas. * All Chi-square tests p < .05
|Gale Copyright:||Copyright 2010 Gale, Cengage Learning. All rights reserved.|