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. Author manuscript; available in PMC: 2021 Apr 1.
Published in final edited form as: Appetite. 2019 Dec 3;147:104546. doi: 10.1016/j.appet.2019.104546

Applying the Information-Motivation-Behavioral Skills Model to Explain Adolescents’ Fruits and Vegetables Consumption

Sasha A Fleary 1, Patrece Joseph 2, Hong Chang 3
PMCID: PMC6957757  NIHMSID: NIHMS1546244  PMID: 31809812

Abstract

Inadequate fruits and vegetables consumption in adolescence increases adolescents’ current and future chronic disease risk and is predictive of inadequate consumption in adulthood. Given that adolescents’ engagement in dietary behaviors is complicated by intrapersonal, interpersonal, and environmental factors, a health behavior model of change incorporating all of these factors is most appropriate to inform research and intervention efforts. Yet, common preventive health behavior models used to explain adolescents’ dietary behaviors do not adequately account for these factors. The current study explored the utility of a comprehensive, predictive model, that is the Information-Motivation-Behavioral Skills (IMB) model, for explaining adolescents’ fruits and vegetables consumption in a cross-sectional national sample. Study hypotheses included (1) health information and motivation for fruits and vegetables would directly be related to fruits and vegetables consumption; and (2) the relationship between fruits and vegetables consumption and health information and motivation would be mediated by behavioral skills for consumption. Data from the adolescent diet-related surveys of the Family Life, Activity, Sun, Health, and Eating (FLASHE) study (N = 1,646) were used. Structural equation modeling was used to test study hypotheses. Fruits and vegetables-related information and motivation were positively related to adolescents’ fruits and vegetables consumption. For information and personal motivation (specifically fruits and vegetables preferences), the relationship with fruits and vegetables consumption was partially mediated through behavioral skills. These preliminary findings support the utility of the IMB model to explain adolescents’ fruits and vegetables consumption.

Keywords: fruits and vegetables; adolescents; Family Life, Activity, Sun, Health, and Eating [FLASHE] study; information-motivation-behavioral skills model

Approximately 69% and 73% of US adolescents do not eat at least two fruits and vegetables daily, respectively (Kann et al., 2018). Inadequate consumption of fruits and vegetables is associated with increased obesity (Wall et al., 2018) and other chronic disease risk (Carter, Gray, Troughton, Khunti, & Davies, 2010; Zhan et al., 2017). Further, adolescents’ health behaviors are positively predictive of their adult health behaviors (Laska, Larson, Neumark-Sztainer, & Story, 2012; Sawyer et al., 2012) and chronic disease status (Frazier, Li, Cho, Willett, & Colditz, 2004; Linos, Willett, Cho, Colditz, & Frazier, 2008; Wang, Chyen, Lee, & Lowry, 2008). Therefore, it is imperative that these behaviors are addressed if we are to reduce the incidence of chronic disease and reverse the predicted trend of today’s youth living shorter lives than their parents (Olshansky et al., 2005; Reither, Olshansky, & Yang, 2011).

Engagement in dietary behaviors is complicated by intrapersonal (e.g., intentions), interpersonal (e.g., social norms), and environmental factors (Davison & Birch, 2001; Story, Neumark-Sztainer, & French, 2002). For example, a systematic review of the literature by McClain, Chappuis, Nguyen-Rodriguez, Yaroch, and Spruijt-Metz (2009) found that perceived modeling, dietary intentions, dietary norms, and food preferences were consistently positively associated with adolescent eating behavior. Further, Neumark-Sztainer, Wall, Perry, and Story (2003) and Granner and Evans (2011) identified home availability and taste preferences as correlates of adolescents’ fruits and vegetable intake. Odum, Housman, and Williams (2018) also found that food preference, self-efficacy, perceived barriers, and normative beliefs about fruit and vegetable consumption were related to adolescents’ fruits and vegetable consumption. Cutler, Flood, Hannan, and Neumark-Sztainer (2011) and Kalavana, Maes, and De Gucht (2010) found that parent/caregiver and peer support and norms were associated with adolescent healthy eating behaviors. Stok, de Vet, de Ridder, and de Wit (2012) and Kalavana et al. (2010) also highlighted the role of self-regulation, an intrapersonal factor, in adolescents’ dietary behaviors. Most of these studies failed to specify a model of change that is inclusive of all the factors outlined. Rather, most studies focus on one or two determinants of adolescents’ dietary behaviors.

Regarding specific theories, studies using the theory of planned action (which focuses on behavioral intention as a predictor of behavior) have supported the role of attitude and perceived behavioral control in behavioral intentions (Blanchard et al., 2009; Hackman & Knowlden, 2014). Further those utilizing the socioecological model (a descriptive model that conceptualizes the nested systems that impact behavior) have identified determinants on each level but fail to provide a theory of change or predictive model to inform or test interventions (DeJong, van Lenthe, van der Horst, & Oenema, 2009; Pearson, Ball, & Crawford, 2011b; Pearson, Griffiths, Biddle, Johnston, & Haycraft, 2017).

Given the lack of research utilizing change models that incorporate multiple determinants of adolescents’ dietary behaviors, the current study explored the utility of the Information-Motivation-Behavioral Skills (IMB) model (Fisher, Fisher, & Harman, 2003), a comprehensive, predictive model of behavior change, in explaining the role of interpersonal, intrapersonal, and environmental factors on adolescents’ fruits and vegetables consumption. The IMB model distinguishes three core determinants of performance of health behaviors: health-related information that can be translated into behavior, personal and social motivation, and behavioral skills that facilitate engagement in behavior (Fisher et al., 2003). According to the IMB model, the extent to which a person has the necessary health-related information, motivation to act on that information, and the required behavioral skills to perform the behavior will determine their behavior engagement. Health-related information and motivation work primarily through behavioral skills to influence behavior when the skills necessary to perform the behavior are complex (e.g., multiple steps, requires engagement with others). However, information and motivation may directly impact behavior when only basic skills are needed to engage in the health behavior (e.g., eating fruits already placed in front of the person). See Figure 1 for an illustration of the IMB model as proposed by Fisher et al. (2003).

Figure 1.

Figure 1.

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Information-Motivation-Behavioral Skills Model taken from Fisher, Fisher, & Harman (2003)

Several studies focus on adolescents’ health-related information (Naghashpour, Shakerinejad, Lourizadeh, Hajinajaf, & Jarvandi, 2014; Rosemond, Blake, Jenkins, Buff, & Moore, 2015; Watson, Kwon, Nichols, & Rew, 2009) to explain their health behaviors, however the IMB model suggests that health-related information alone is insufficient for behavior change. For example, adolescents’ ability to read labels or identify the accurate number of servings of fruits and vegetables they should consume does not automatically translate into behavior though having this information could help with decision-making.

Motivation, another construct in the IMB model, comprises of both personal and social motivation. Personal motivation is a complex issue for adolescents – it refers to beliefs and evaluations of consequences for engaging in the behavior. Adolescence is a period of increased risk-taking despite high risk perception (Steinberg, 2007), this is partly explained by adolescents’ personal fable of “bad things can happen to them not me” (Alberts, Elkind, & Ginsberg, 2007; Elkind, 1967). Given that dietary behaviors are unlikely to have immediate consequences or result in noticeably drastic changes in the short-term, adolescents may place less value on these behaviors as opposed to behaviors in which there is immediate gratification (Wulfert, Block, Santa Ana, Rodriguez, & Colsman, 2002) and thus have lower personal motivation for engaging in the behaviors. Social motivation includes perceived and actual social support from parents and peers for engagement in behaviors and adolescents’ responsiveness to wanting to comply with their wishes.

Behavioral skills, the last predictive construct in the IMB model, includes perceived and objective skills for carrying out the behavior as well as confidence (i.e., self-efficacy) for engagement in the behavior. Lack of these skills may be barriers to engagement in health behaviors. Kelly, Melnyk, Jacobson, and O’Haver (2011) found that perceived difficulty was negatively related to adolescents’ behavioral skills use for healthy eating, whereas Bruening et al. (2010) identified perceived barriers as a mediator in the relationship between self-efficacy beliefs and fruits and vegetables intake. As adolescents’ self-efficacy beliefs increased, their perceived barriers decreased, and their fruits and vegetable intake increased. Further, interventions that address adolescents’ barriers to fruits and vegetables intake (e.g., offering more options and free or reduced cost fruits and vegetables) have proven successful at improving adolescents’ fruits and vegetable intake (Bogart et al., 2014; Di Noia & Contento, 2010; Gosliner, 2014).

The IMB model has been widely used in the HIV prevention research as well as in cancer prevention and screening research with much success for describing and intervening on behaviors (Aronowitz & Munzert, 2006; Fisher, Fisher, Bryan, & Misovich, 2002; Fisher, Williams, Fisher, & Malloy, 1999; Scott-Sheldon et al., 2010). Few studies have applied the IMB model to dietary behaviors in adolescents and the limited studies support the proposed theoretical relationships in the IMB model for adolescents’ mindful eating behaviors (Daly, Pace, Berg, Menon, & Szalacha, 2016) and for adolescents’ fruits and vegetables eating behaviors (Kelly, Melnyk, & Belyea, 2012); however none of these studies utilized a national sample.

The purpose of the current study is to test the utility of the IMB model to explain adolescents’ fruits and vegetables consumption using a cross-sectional national dataset. Figure 2 illustrates the hypothesized pathways assessed in this study. Specifically, we hypothesized that information about and motivation for consuming fruits and vegetables would be directly positively related to fruits and vegetables consumption (Figure 2, arrows a–e) and behavioral skills for consuming fruits and vegetables (Figure 2, arrows f–j). We also hypothesize that the relationship between information and motivation for consuming fruits and vegetables and reported fruits and vegetables consumption will be mediated (partially or fully) by behavioral skills, or related through an indirect pathway.

Figure 2.

Figure 2.

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Adapted Information-Motivation-Behavioral Skills Model for the Current Study.

Methods

Study Population and Design

This study was exempt from Institutional Review Board (IRB) approval as it is a secondary data analysis of a publicly available de-identified dataset. The original study was approved by the US Government’s Office of Management and Budget, NCI’s Special Studies IRB, and Westat’s IRB. Data were extracted from the National Cancer Institute’s Family Life, Activity, Sun, Health, and Eating study (FLASHE; National Cancer Institute, 2016). FLASHE, a national cross-sectional study conducted through a web platform between April and October 2014, assessed psychosocial, generational, and environmental correlates of cancer prevention behaviors. Parent/caregiver-adolescent dyads (N=5,027) were invited through the Ipsos Consumer Opinion Panel (Oh et al., 2017). Ipsos selected a balanced sample that was similar in sex, census division, race/ethnicity, household income, and household size of the general US population. Selected panelists were screened for eligibility (adult ≥18 years living with a 12–17-year-old ≥50% of the time who agreed to be contacted for the study) via a short web survey. Eligible dyads were randomly assigned to the survey only or motion study (not discussed here; Oh et al., 2017). Eligible parents/caregivers (hereafter referred to as parents) were invited via email to participate in the study and adolescents were only invited to participate if parents consented to participation for the dyad. A separate email invitation was sent to adolescents’ personal email accounts requesting assent to participate. The study enrollment rate was 38.7% and 85.6% of dyads in the survey only group completed all surveys. Parents and their adolescents completed web-based surveys on cancer prevention behaviors and incentives ranged from $5–$10 per survey. Data from the adolescent diet-related behaviors survey were used in this study. More information about the study is available on the FLASHE website and methodology report (National Cancer Institute, 2016).

Measures

Demographics.

Demographics included age, sex, race, free/reduced lunch recipient status, and fruits and vegetable availability. Age ranged from 12–17-years-old. Options for sex were male and female. Race/ethnicity was assessed using two questions: Are you Hispanic, Latino/a, or Spanish origin, and which one or more of the following would you say is your race? Participant responses were dichotomized into White and non-White (i.e., Hispanic, non-Hispanic Black or African-American and non-Hispanic Other). Parents indicated whether adolescents received free or reduced lunch. Fruits and vegetables availability assessed how often fruits and vegetables were available in adolescents’ homes. Responses were on a 5-point scale and ranged from 1 (never) to 5 (always). This item was drawn from the 2010 National Youth Physical Activity and Nutrition Survey (Centers for Disease Control and Prevention, 2010) and the Project Eat-II survey (Neumark-Sztainer et al., 2007).

Information.

One item was used to assess whether adolescents knew the recommended daily serving of fruits and vegetables. Adolescents were able to enter any number as a response and responses were dichotomized into correct (responses between 3–5 servings a day) and incorrect (all other responses). This knowledge of fruits and vegetable recommendation item was drawn from the Food Attitudes and Behaviors Survey (FAB; Erinosho et al., 2015).

Motivation.

Thirteen items assessed adolescents’ personal and social motivation for fruits and vegetables consumption. Four items measuring adolescents’ motivation for eating fruits and vegetables (e.g., I would eat fruits and vegetables every day because: I would feel bad about myself if I didn’t) were taken from the Self-Determination Theory Self-Regulation Questionnaires (Levesque et al., 2006). Response options ranged from 1 (strongly disagree) to 5 (strongly agree). The three items assessing adolescents’ food preferences for fruit drinks, fruit, and vegetables were developed for the purposes of the FLASHE study. Response options were on a 6-point scale ranged from 1 (strongly dislike) to 5 (strongly like) and included 6 (never tried it). Respondents who endorsed “6 – never tried it” were excluded from the analyses (n=22, 18 respondents ‘never tried’ 1 item and 4 ‘never tried’ 2 items). Six items measured parents’ food parenting practices around adolescents’ fruits and vegetables consumption (e.g., My parents encourage me to try different kinds of fruits and vegetables). Response options were on a 5-point scale and ranged from 1 (strongly disagree) to 5 (strongly agree). Parent food parenting practices items were modified or taken from the Child Feeding Questionnaire (Birch et al., 2001), Comprehensive Feeding Practices Questionnaire (Musher-Eizenman & Holub, 2007), Parental Feeding Style Questionnaire (Wardle, Sanderson, Guthrie, Rapoport, & Plomin, 2002), and the Legitimacy of Parental Authority (Darling, Cumsille, & Martínez, 2008) measure.

Behavioral skills.

Five items were used to assess adolescents’ behavioral skills for consuming fruits and vegetables. These items specifically addressed adolescents’ barriers to eating fruits and vegetables (e.g., I don’t eat fruits and vegetables as much as I like to because they spoil before I get a chance to eat them). Response options for these items were on a 5-point scale ranging from 1 (strongly disagree) to 5 (strongly agree) and responses were reverse coded. Items were drawn from the FAB study (Erinosho et al., 2015).

Fruits and Vegetables Consumption.

Fruits and vegetables consumption was computed based on adolescents’ indication of the number of days they ate fruits, vegetables (i.e., potatoes, salad, and other vegetables), and drank 100% fruit juice over the past seven days. The items for adolescents’ fruits and vegetables consumption were taken from the Dietary Screener Questionnaire in the 2010 National Youth Physical Activity and Nutrition Survey (Center for Disease Control and Prevention, 2010). Responses were on a 6-point scale and were recoded into per day consumption according to the 2010 National Youth Physical Activity and Nutrition Survey (Center for Disease Control and Prevention, 2010): I did not eat or drink the item= 0/day, 1–3 times/week= .286/day, 4–6 times/week= .714/day, 1 time per day = 1/day, 2 times per day = 2/day, and 3 or more times = 3/day. Adolescents’ daily fruits, vegetables, and 100% fruit juice consumption were summed to create a fruits and vegetables consumption variable.

Statistical Analyses

Structural equation model (SEM) was used to test the study hypotheses that fruits- and vegetables-related information and motivation would be positively related to fruits and vegetables consumption, and that the relationships would be mediated through behavioral skills. SEM was used to examine the relationship among latent constructs depicted in the conceptual model (Figure 2). To simplify the model fitting procedure, the SEM was fitted in two steps. First, a measurement model was fitted using EFA to derive the latent factor structures on motivation and behavior skills. Four factors were identified through exploratory factor analysis (EFA) for the thirteen motivation items: internal motivation, personal preference, parental passive support, and parental active support. Internal motivation was characterized by items related to adolescents’ reasons for eating fruits and vegetables. The personal preference factor included items related to adolescents’ like or dislike of fruits and vegetables. Parental passive support included items on the ways in which parents supported adolescents’ fruits and vegetables consumption through modeling and provision of resources for the adolescent to eat fruits and vegetables. The parental active support factor included items related to the ways in which parents intervened to encourage or compel adolescents to eat fruits and vegetables. One item, preference for 100% fruit juice, did not load on any of the four factors. To increase the interpretability of the underlying scale structures, all four motivation scales were scored using Likert scaling methods then converted to the 0 to 100 range. Higher scores on each of these four scales indicate more positive attitude towards the underlying concepts. The five behavior skills items were used in the EFA and one factor was identified. A 0–100 Likert scale score was created and the higher scores indicate more behavioral skills (or less barriers) to consume fruits and vegetables. The scale scores to quantify these latent constructs were then created and used in the SEM to examine the relationship among them. See Appendix I for rotated factor loadings (orthogonal varimax rotations) and eigenvalues (measure of adequacy of each factor) from the EFAs, and Cronbach alphas for resultant scales.

Second, the regression-based analysis for SEM was estimated. The SEM produced both direct and indirect effects on fruits and vegetables consumption through hypothesized pathways. The model was also adjusted by potential moderating variables including social-demographic characteristics (age, sex, race free/reduced lunch status – proxy for income) and one other environmental variable (fruits and vegetables availability in the home). To help with the interpretability of the model coefficients, a standardized version of SEM coefficients were produced and presented. The model fitting indices were also obtained to assess the model fitting adequacy. The raked sampling weights for adolescents responding to the diet surveys were used in the SEM analyses so that inferences may be made to the US population. We specified the robust estimation for the variance-covariance matrix in an SEM fitting process so that the model could be estimated under more relaxed assumptions (i.e., no requirement for normal distribution and identically distributed from the current observation to the next). All statistical analyses were carried out using STATA (StataCorp, 2017).

Results

A total of 1,646 adolescents (Mean age = 15.5, SD = 1.6) were included in the analyses. The sample was predominantly White (64.1%), female (50.5%), and non-recipients of free/reduced lunch (69.6%). Adolescents consumed an average of 2.1 fruits and vegetables per day. All created scales had acceptable Cronbach alphas. Descriptive statistics for the variables used in the SEM are presented in Table 1 and factor loadings and Cronbach alphas for IMB model constructs are presented in Appendix I.

Table 1:

Descriptive characteristics of variables used in the structural equation model predicting adolescents’ fruits and vegetables intake

Variable Missing Values N (%) Mean (SD) or N (%) Min Max
 N=1646
Informationa (Mean(SD))
 F&V Recommendations Knowledge 6 (0.4%) 0.4 (0.5) 0.0 1.0
Behavioral skillsb (Mean(SD))
 Behavioral Skills Composite 2 (0.1%) 58.8 (21.5) 0.0 100.0
  They often spoil before I get a chance to eat them 4 (0.2%) 3.5 (1.3) 1.0 5.0
  They aren’t filling enough 7 (0.4%) 3.2 (1.3) 1.0 5.0
  The restaurants I go to don’t serve F&V 11 (0.7%) 3.6 (1.2) 1.0 5.0
  I just don’t think of F&V when I am looking for something to eat 6 (0.4%) 2.8 (1.4) 1.0 5.0
  They are not packed in my lunch 9 (0.5%) 3.7 (1.2) 1.0 5.0
Motivation (Mean(SD))
 Parental Active Support Compositeb 3 (0.2%) 57.5 (28.4) 0.0 100.0
  My parent(s) & I decide together how many F&V I have to eat 12 (0.7%) 3.1 (1.3) 1.0 5.0
  My parent(s) have to make sure that I eat enough F&V 12 (0.7%) 3.4 (1.3) 1.0 5.0
  My parent(s) make me eat F&V 12 (0.7%) 3.4 (1.4) 1.0 5.0
 Parental Passive Support Compositeb 3 (0.2%) 82.6 (19.4) 0.0 100.0
  My parent(s) buy F&V for me 4 (0.2%) 4.5 (0.8) 1.0 5.0
  My parent(s) try to eat F&V when I’m around 7 (0.4%) 4.1 (1.1) 1.0 5.0
  My parent(s) encourage me to try different kinds of F&V 14 (0.9%) 4.3 (0.9) 1.0 5.0
 Internal Motivation Compositeb 2 (0.1%) 61.2 (20.7) 0.0 100.0
  I would feel bad about myself if I didn’t [eat F&V everyday] 4 (0.2%) 2.9 (1.2) 1.0 5.0
  I have thought about it & decided that I want to eat F&V everyday 4 (0.2%) 3.6 (1.1) 1.0 5.0
  Others would be upset with me if I didn’t [eat F&V] 5 (0.3%) 3.0 (1.3) 1.0 5.0
  It is an important thing for me to do 10 (0.6%) 4.2 (0.9) 1.0 5.0
 Personal Preference Compositec 4 (0.2%) 77.7 (22.4) 0.0 100.0
  Fruit like apples, bananas, melon, etc. 15 (0.9%) 4.4 (0.9) 1.0 5.0
  A green salad, or other non-fried vegetables like carrots, broccoli, green beans, corn, etc. 20 (1.2%) 3.9 (1.2) 1.0 5.0
Fruits and Vegetables Consumptiond (Mean(SD))
 Total F&V Consumption 56 (3.4%) 2.1 (1.7) 0.0 12.0
Modification Variables
 Age (Mean(SD)) 19 (1.2%) 15.5 (1.6) 13.0 18.0
 Male (N(%)) 23 (1.4%) 804 (49.5) 0.0 1.0
 White (N(%)) 35 (2.1%) 1033 (64.1) 0.0 1.0
 Free or reduced lunch eligibility (N(%)) 2 (0.1%) 499 (30.4) 0.0 1.0
 F&V available in homee (Mean(SD)) 7 (0.4%) 4.3 (0.9) 1.0 5.0
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F&V= fruit and vegetable; Max = maximum score; Min = minimum score;

a

Mean and standard deviation. Participants indicated any number for servings of fruits and vegetables. Responses were dichotomized into correct (responses between 3–5 servings a day) and incorrect (all other responses).

b

Mean and standard deviation on a 5-point scale. Participants indicated their agreement with each statement on a 5-point Likert scale (1 = strongly disagree, 2 = somewhat disagree, 3 = neither disagree or agree, 4 = somewhat agree, 5 = strongly agree).

c

Mean and standard deviation on a 5-point scale. Participants indicated their personal preference for foods and drinks on a 5-poingt Likert scale (1 = strongly dislike, 2 = somewhat dislike, 3 = neither dislike or like, 4 = somewhat like, 5 = strongly like).

d

Mean and standard deviation of total score. Participants indicated their frequency of fruits, fruit juice, and vegetables intake on a 6-point scale converted to daily intake: I did not eat “item” during the past 7 days = 0/day, 1–3 times per week = 0.286/day, 4–6 times per week = 0.714/day, 1 time per day = 1/day, 2 times per day = 2/day, 3 or more times per day = 3/day).

e

Mean and standard deviation on a on a 5-point scale. Participants indicated the availability of fruits and vegetables in their home on a 5-point Likert scale (1= never, 2 = rarely, 3 = sometimes, 4 = often, 5 = always).

Regarding model adequacy, the coefficient of determination (equivalent to the R-squared) for the SEM was 0.30 and standardized root-mean-squared residual was 0.018. Both indicated satisfactory goodness of fit. The standardized model coefficients are presented in Table 2. The unstandardized model coefficients are presented in Appendix II.

Table 2.

Standardized results of the structural equation model estimating direct and indirect effects on adolescents’ fruits and vegetables consumption

Direct Effect Indirect Effect Total Effect
Variable β 95% CI P β 95% CI P β 95% CI P
Dependent Variable Behavioral Skills
Motivation
 Parental Active Support −0.060 −0.123 – 0.002 0.06 No Path -- -- −0.060 −0.123 – 0.002 0.06
 Parental Passive Support 0.150 0.087 – 0.212 <0.001 No Path -- -- 0.150 0.087 – 0.212 <0.001
 Internal Motivation −0.001 −0.065 – 0.064 0.99 No Path -- -- −0.001 −0.065 – 0.064 0.99
 Personal Preference 0.298 0.245 – 0.351 <0.001 No Path -- -- 0.298 0.245 – 0.351 <0.001
Information
 F&V Recommendations Knowledge 0.071 0.020 – 0.122 <0.01 No Path -- -- 0.071 0.020 – 0.122 <0.01
Dependent Variable F&V Consumption
 Parental Active Support 0.068 0.011 – 0.125 0.02 −0.009 −0.018 – 0.001 0.09 0.059 0.003 – 0.116 0.04
 Parental Passive Support 0.019 −0.0380 – 0.076 0.51 0.021 0.010 – 0.033 <0.001 0.040 −0.016 – 0.097 0.16
 Internal Motivation 0.200 0.143 – 0.256 <0.001 <−0.001 −0.010 – 0.010 0.98 0.200 0.143 – 0.256 <0.001
 Personal Preference 0.245 0.200 – 0.290 <0.001 0.043 0.025 – 0.060 <0.001 0.288 0.241 – 0.335 <0.001
Information
 F&V Recommendations Knowledge 0.061 0.012 – 0.110 0.02 0.010 0.002 – 0.018 0.01 0.071 0.022 – 0.120 <0.01
Behavioral Skills
 F&V Consumption Self-Efficacy 0.143 0.090 – 0.196 <0.001 No Path -- -- 0.143 0.090 – 0.196 <0.001
Modification Variables
 Age −0.009 −0.058 – 0.040 0.72 No Path -- -- −0.009 −0.058 – 0.040 0.72
 Male 0.008 −0.039 – 0.055 0.74 No Path -- -- 0.008 −0.039 – 0.055 0.74
 White 0.018 −0.029 – 0.065 0.46 No Path -- -- 0.018 −0.029 – 0.065 0.46
 Free/reduced Lunch Recipient −0.047 −0.098 – 0.004 0.07 No Path -- -- −0.047 −0.098 – 0.004 0.07
 F&V Availability 0.085 0.028 – 0.142 <0.01 No Path -- -- 0.085 0.028 – 0.142 <0.01
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F&V= fruits and vegetables; β = standardized regression coefficient

As hypothesized, adolescents’ information, motivation, and behavioral skills were associated with their fruits and vegetables consumption through various pathways. First, the information about daily fruits and vegetables intake recommendations was positively related to fruits and vegetable consumption directly through pathway a (Figure 2) and also positively directly related to behavioral skills (path f, Figure 2). Behavioral skills were positively directly related to fruits and vegetables consumption through path k (Figure 2). The information about daily fruits and vegetables recommendations was also indirectly related to fruits and vegetables consumption through behavioral skills (path f and k, Figure 2) suggesting partial mediation. The total effect was significant.

For social motivation, only parental passive support was positively directly related to behavioral skills (path h, Figure 2). Parental passive support was not significantly related to adolescents’ fruits and vegetables consumption. Parental active support was positively directly related to fruits and vegetables consumption (path e, Figure 2) but the relationship was not mediated by behavioral skills. Regarding personal motivation, personal preference was positively directly related to behavioral skills (path j, Figure 2) and fruits and vegetables consumption (path b, Figure 2). Personal preference was also indirectly related to fruits and vegetables consumption through behavioral skills (path j and k, Figure 2) suggesting partial mediation. The total effect was significant. Internal motivation was unrelated to behavioral skills but was positively related to fruits and vegetables consumption (path c, Figure 2).

Discussion

The purpose of this study was to test the usefulness of the IMB model for explaining adolescents’ fruits and vegetables consumption using a cross-sectional national dataset. The findings support our hypotheses that adolescents’ information about and motivation for consuming fruits and vegetables would be directly positively related to fruits and vegetables consumption and behavioral skills for consuming fruits and vegetables and that these relationships would be mediated by behavioral skills. These findings suggest that the IMB model is an appropriate model for explaining adolescents’ fruits and vegetables consumption.

Parental support was associated with adolescents’ fruits and vegetables consumption; however, there were notable differences in this relationship based on type of support. Parental active support was associated with higher fruits and vegetables consumption in adolescents but unrelated to behavioral skills, while parental passive support was positively related to behavioral skills but unrelated to fruits and vegetables consumption. These results suggest that both types of support are valuable; passive support likely encourages self-efficacy while active support provides the resources for behavior engagement. Further, given that the IMB model proposes that when the behavior is simple, motivation may influence the behavior directly, it is not surprising that parental active support was not mediated by behavioral skills. Active support may reduce the number of steps adolescents need to go through in order to consume fruits and vegetables. Parental active support may also be unrelated to behavioral skills because active support delays the development of adolescents’ self-efficacy for engaging in fruits and vegetables consumption as practice is important to self-efficacy beliefs (Loth, MacLehose, Fulkerson, Crow, & Neumark-Sztainer, 2013). It is also likely that parents with adolescents who have low self-efficacy or face increased barriers to consuming fruits and vegetables may be more intentional and active to ensure their adolescents are consuming adequate amounts of fruits and vegetables (Pearson, Ball, & Crawford, 2011a). Future longitudinal studies should examine the causal relationships between parental support, adolescents’ behavioral skills, and fruits and vegetables consumption to inform effective intervention strategies for parent-adolescent dyads.

As expected, information and personal preference for fruits and vegetables were positively related to fruits and vegetables consumption and the relationships were partially mediated by behavioral skills. Multiple studies support that food preferences and health knowledge/information predict food consumption behavior (Larson, Laska, Story, & Neumark-Sztainer, 2012; Racey et al., 2017). The findings of the current study provide further support for intervening on children and adolescents’ food preferences and information/knowledge to influence behavior and efficacy for engaging in fruits and vegetables consumption. Further, the positive relationship between adolescents’ internal motivation and fruits and vegetables consumption reiterates the importance of adolescents’ ‘buy-in’ to the value of eating fruits and vegetables to their behavior and their efficacy for engaging in the behavior.

Overall our findings suggest that the IMB model is an appropriate model for explaining adolescents’ fruits and vegetables consumption behaviors. These findings suggests that, similar to research using IMB model to explain other health behaviors (e.g., (Aronowitz & Munzert, 2006; Scott-Sheldon et al., 2010), the IMB model may be suitable for informing, designing, and assessing interventions that simultaneously address knowledge, behavioral skills, and interpersonal and intrapersonal contributors to adolescents’ fruits and vegetables consumption behaviors. However, this study is a preliminary analysis and is cross-sectional. Future studies should explore the IMB model using expanded definitions and measurements of all the constructs. Future studies should also utilize a longitudinal study design in order to assess the IMB model’s utility as a model of eating behavior change for adolescents with consideration of the variation in adolescents’ capacity, motivation, and autonomy for health behaviors as they progress from early adolescence to late adolescence. Given the complex nature of adolescents’ fruits and vegetables consumption and the multiple influences on their behavior (e.g., peers, parents, media, environment, developmental characteristics; Bruening et al., 2012; Chung, Ersig, & McCarthy, 2017; Cutler et al., 2011; Fleary & Ettienne, 2019; Freisling, Haas, & Elmadfa, 2010), future studies should consider using a bioecological model to inform other variables that are a part of the motivation and behavioral skills constructs and identify contextual mediators and moderators to further explain the model.

The current study is not without limitations. The limited measurement of the constructs, specifically information and fruits and vegetables consumption, limits the interpretability of our findings. Similarly, our measurement of behavioral skills was limited by the dataset. We focused on barriers adolescents faced for eating fruits and vegetables as indicators of poor behavioral skills (e.g., not eating fruits and vegetables before they spoil may be indicative of inability to meal plan). Behavioral skills such as the ability to plan and prepare meals, advocate for the availability for fruits and vegetables as well as self-efficacy for engaging in behavioral skills should be explored in future research. Further, race was dichotomized into white and non-white and was not a significant covariate. However, exploration of non-white racial groups separately may produce different results. Future studies should include a sufficient sample to explore the effect of race on the relationships between the constructs in the IMB model. Another limitation is that the data were self-reported by adolescents. Self-report data are subject to desirability and recall biases, which also pose a threat to the validity of the findings. Though the data analyses were completed using SEM and a path model was developed, the data used are cross-sectional and causal relationships should not be implied.

Conclusion

This study assessed the utility of the IMB model in explaining adolescents’ fruits and vegetables consumption using a cross-sectional national dataset. Our findings provide preliminary evidence for the appropriateness of the IMB model in explaining adolescents’ fruits and vegetables consumption. Next steps include longitudinal research to examine the causal relationships between information, motivation, behavioral skills, and adolescents’ fruits and vegetables consumption and the utility of the IMB model to explain diet behavior change in adolescents.

Acknowledgments

This study utilized data from the National Cancer Institute. The findings and conclusions of this study are those of the authors.

Funding: This work was supported by the National Institute of Health [grant number 1K12HD092535].

Appendix II.

Non-standardized results of the structural equation model estimating direct and indirect effects on adolescents’ fruits and vegetables consumption

Direct Effect Indirect Effect Total Effect
Variable B 95% CI P B 95% CI P B 95% CI P
Dependent Variable Behavioral Skills
Motivation
 Parental Active Support −0.046 −0.093 – 0.001 0.06 No Path -- -- −0.046 −0.093 – 0.001 0.06
 Parental Passive Support 0.166 0.096 – 0.237 <0.001 No Path -- -- 0.166 0.096 – 0.237 <0.001
 Internal Motivation −0.001 −0.067 – 0.066 0.99 No Path -- -- −0.001 −0.067 – 0.066 0.99
 Personal Preference 0.287 0.236 – 0.338 <0.001 No Path -- -- 0.287 0.236 – 0.338 <0.001
Information
 F&V Recommendations Knowledge 3.202 0.883 – 5.521 <0.01 No Path -- -- 3.202 0.883 – 5.521 <0.01
Dependent Variable F&V Consumption
Motivation
 Parental Active Support 0.004 <0.001 – 0.008 0.04 −0.001 −0.001 - −0.001 . 0.004 <−0.001 – 0.008 0.07
 Parental Passive Support 0.002 −0.004 – 0.008 0.57 0.002 <−0.001 – 0.004 0.06 0.004 −0.002 – 0.010 0.23
 Internal Motivation 0.017 0.013 – 0.021 <0.001 No Path 0.017 0.013 – 0.021 <0.001
 Personal Preference 0.019 0.015 – 0.023 <0.001 0.003 0.001 – 0.005 <0.01 0.022 0.018 – 0.026 <0.001
Information
 F&V Recommendations Knowledge 0.219 0.045 – 0.393 0.01 0.037 0.007 – 0.066 0.01 0.256 0.077 – 0.434 <0.01
Behavioral Skills
 F&V Consumption Self-Efficacy 0.012 0.008 – 0.015 <0.001 No Path -- -- 0.012 0.008 – 0.015 <0.001
Modification Variables
 Age −0.010 −0.063 – 0.043 0.72 No Path -- -- −0.010 −0.063 – 0.043 0.72
 Male 0.028 −0.135 – 0.190 0.74 No Path -- -- 0.028 −0.135 – 0.190 0.74
 White 0.064 −0.105 – 0.232 0.46 No Path -- -- 0.064 −0.105 – 0.232 0.46
 Free/reduced Lunch Recipient −0.175 −0.364 – 0.013 0.07 No Path -- -- −0.175 −0.364 – 0.013 0.07
 F&V Availability 0.162 0.054 – 0.270 <0.01 No Path -- -- 0.162 0.054 – 0.270 <0.01
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F&V= fruits and vegetables; B = unstandardized regression coefficient

Appendix I.

Eigenvalues and rotated factor loadings of the exploratory factor analyses and Cronbach alphas for constructs in the structural equation model

Item Cronbach alpha Eigenvalues Rotated Factor Loadingsa
Behavioral skills
 Behavioral Skills Construct 0.71 1.58
  They often spoil before I get a chance to eat them 0.55
  They aren’t filling enough 0.64
  The restaurants I go to don’t serve F&V 0.52
  I just don’t think of F&V when I am looking for something to eat 0.56
  They are not packed in my lunch 0.52
Motivation
 Parental Active Support Construct 0.81 3.62
  My parent(s) & I decide together how many F&V I have to eat 0.62
  My parent(s) have to make sure that I eat enough F&V 0.78
  My parent(s) make me eat F&V 0.72
 Parental Passive Support Construct 0.78 1.38
  My parent(s) buy F&V for me 0.62
  My parent(s) try to eat F&V when I’m around 0.68
  My parent(s) encourage me to try different kinds of F&V 0.66
 Internal Motivation Construct 0.69 0.63
  I would feel bad about myself if I didn’t [eat F&V everyday] 0.65
  I have thought about it & decided that I want to eat F&V everyday 0.61
  Others would be upset with me if I didn’t [eat F&V] 0.36
  It is an important thing for me to do 0.46
 Personal Preference Construct 0.65 0.39
  Fruit like apples, bananas, melon, etc. 0.62
  A green salad, or other non-fried vegetables like carrots, broccoli, green beans, corn, etc. 0.60
Open in a new tab

F&V = fruits and vegetables

a

Orthogonal varimax rotations

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Declaration of interest statement: The authors have no conflict of interest to declare

Contributor Information

Sasha A. Fleary, Eliot-Pearson Department of Child Study and Human Development, 574 Boston Ave, Room 211C, Tufts University, Medford, MA USA.

Patrece Joseph, Tufts University.

Hong Chang, Tufts Medical Center.

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