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Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Disclosure
  9. REFERENCES

Diet-related chronic diseases are at epidemic levels in low-income ethnic minority populations. The purpose of this study is to decrease risk for obesity in children by modifying the food environment and conducting point-of-purchase promotions that will lead to changes in psychosocial factors and behaviors associated with healthier food choices among low-income communities with a preponderance of Native Hawaiians and Pacific Islanders. We implemented an intervention trial over a 9–11-month period in five food stores in two low-income multiethnic communities in Hawaii, targeting both children and their adult caregivers. The Healthy Foods Hawaii (HFH) intervention consisted of an environmental component to increase store stocking of nutritious foods, point-of-purchase promotions, interactive sessions, and involved local producers and distributors. We evaluated the impact of the program on 116 child–caregiver dyads, sampled from two intervention and two comparison areas before and after intervention implementation. Program impacts were evaluated using multivariable linear regression. The HFH program had a significant impact on caregiver knowledge and the perception that healthy foods are convenient. Intervention children significantly increased their Healthy Eating Index (HEI) score for servings of grains, their total consumption of water, and showed an average 8.5 point (out of 90 total, eliminating the 10 points for variety, giving a 9.4% increase) increase in overall HEI score. A food store intervention was effective in improving healthy food knowledge and perception that healthy foods are convenient among caregivers, and increased the consumption of several targeted healthy foods by their children. Greater intensity, sustained food system change, and further targeting for children are needed to show greater and sustained change in food-related behaviors in low-income Native Hawaiian and Pacific Islander communities.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Disclosure
  9. REFERENCES

National survey data from the United States show that the prevalence of overweight and obesity among adults began to increase around the mid-1980s and continues to increase (1). From 1963 to 1980 the prevalence of overweight among children and adolescents changed relatively little, but showed a noticeable increase from 1988 to 1994, and this trend is continuing (2,3,4,5).

Asian, Native Hawaiian, and Pacific Islander children are not separately identified in national studies of obesity prevalence. In the Pacific Islands, rates of obesity and overweight in adults are among the highest in the world (6,7). Native Hawaiians and Pacific Islanders are at the greatest risk of all ethnic groups in the Pacific for obesity and obesity-related diseases (8,9). High school children living in Hawaii are somewhat more likely to be obese than national averages (15.6% vs. 13.0%, respectively). Available data on child BMI in the Pacific region also find that ∼30% of children are overweight or obese (6,10,11).

Food intake data on Pacific Islander children are limited (12). In a study of a multiethnic group of adolescent girls in Hawaii, high levels of saturated fat and sugar intake were observed, as well as low intakes of calcium and fiber (13). A similar preponderance of high-fat processed meat and sugar consumption has been observed among youth in other Pacific Island settings (14,15). Mixed ethnic girls and Asian girls had lower micronutrient intakes than white girls (16).

Health educators have recognized food stores as a promising venue for providing health information and encouraging the purchase and consumption of healthy foods (17). There is strong evidence that healthy food availability in urban supermarkets is associated with improved diet and lower rates of chronic disease (18,19,20,21). Recent evidence supports this role in small food stores as well (22). Programs in food stores have the potential to impact point-of-purchase decision-making regarding household food choices. Supermarket intervention trials have shown success in increasing the purchase of healthy foods and for improving consumer knowledge (17) and recent work has shown limited success in small stores (23,24,25). Most work in food stores has focused on adult consumers, and has not examined the potential for this intervention venue to impact on children.

Healthy Foods Hawaii intervention

The Healthy Foods Hawaii (HFH) intervention aimed to increase the availability of healthy foods in stores in the two intervention communities, and promote healthier food choices and food preparation methods. HFH was conducted in two of Hawaii's communities with a higher prevalence of Native Hawaiian residents: one on the Island of Oahu (February to December 2006) and one on the Island of Hawaii (Big Island) (May 2006 to January 2007). Five stores in the two communities were selected as implementation venues. Two other similar communities on each island were selected to act as comparison communities.

The HFH intervention comprised four phases, each running for 6–8 weeks with a 1–2-week break between phases: the phases were developed in a participative process with community members. Decisions about which foods to focus on were based on review of the literature, and on community workshops which helped to identify those foods that were perceived to be the greatest contributors to fat and total caloric intake. The phases targeted: (i) healthier beverages (water, diet soda, lite nectars, and 100% juices); (ii) healthier snacks for children (whole grain, lower sugar cereals, low-fat milk, fruit and vegetables with low-fat dips, pretzels, and baked chips); (iii) healthier condiments (lite mayonnaise, low-fat salad dressings, and homemade dressings); and (iv) healthier meals (drain and rinse ground meat, lite/low sodium Spam, tuna in water, locally produced “chop suey” mix (bean sprouts and vegetables)).

In stores, posters, educational displays, and shelf labels (Lower in Fat, Lower in Sugar, Healthy Food Choice, Healthy for Keiki (Child), and Local Produce) were used as educational tools. Cooking demonstrations/taste tests were held four to six times per phase at each intervention store, with brochures and recipe cards distributed during the demonstrations/taste tests.

Two local producers and four local distributors were involved in the project. The most common method of producer and distributor collaboration with the project was the provision of product and/or promotional items to support taste tests and cooking demonstrations. In addition, all of the distributors already had products in most of the stores; thus, collaboration centered on changes in product distribution during specific phases. Local producers and their products were highlighted through the use of a “producer biography,” which was hung above their product in the produce section.

Of the four distributors involved in the project, one distributed soft drinks, one snack foods, one milk and milk products, and another acted as both producer and distributor. One or more of the distributors provided product and/or promotional materials in each phase. During phases 1–3, the distributors (for milk, chips, nectar drinks, and prepared vegetables) provided their products for use in the taste test/cooking demonstrations. Some also provided gift certificates and some giveaways for the participants (e.g., pens, visors).

This article presents the impact results of the HFH program on consumers, and addresses the following questions:

  • How did the program impact adult caregiver's psychosocial factors and food-related behaviors?

  • How did the program impact their children's psychosocial factors, food-related behaviors, and food intake?

Methods and Procedures

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Disclosure
  9. REFERENCES

The intervention phase of the study was implemented in two communities on each of Oahu and the Big Island of Hawaii with populations of 10,506 and 5,748, respectively. Comparison communities on each island had populations of 3,664 and 2,297, respectively. These four communities are home to more Native Hawaiian and Pacific Islanders (Oahu 27%, Big Island of Hawaii 10%) than the State of Hawaii as a whole (9% Native Hawaiian and Pacific Islanders). Income levels are low, with >75% of the population below the poverty level (US Census 2000).

Two questionnaires were developed: the Customer Impact Questionnaire (CIQ) and the Child Customer Impact Questionnaire. Both the CIQ and CCIQ were revised several times following review by staff and co-investigators, and piloting on eight adult caregivers and their families in the Oahu communities. The CIQ was adapted from instruments developed for other similar intervention trials (26,27).

Sampling strategy

Respondents from the two sites on Oahu were randomly selected from local health center patient databases, based on the criteria of child having had an encounter with the health-care facility during the past 2 years, and being 8–12 years old at the time of the preintervention interview. The respondents were selected as “caregiver–child” pairs. Health center personnel were provided with lists of potential respondents who fit study criteria and made the initial contact and scheduling to avoid conflict with Health Insurance Portability and Accountability Act privacy rules. Interviews were conducted at the health center or at respondent's houses according to their preference and availability.

Though no health center was located in the selected study area on the Big Island (Hawaii), participants were selected using the same age criteria for the children and caregiver. To provide a sample, Census 2000 data were used to identify the areas within census tracts where children in the appropriate age range were living. A random cluster sampling technique was then used to identify how many children from each census block would be interviewed. Data collectors went systematically from door to door to obtain the specific proportion of respondents from within each selected census block until willing respondents that fit study criteria were identified. Overall response rate was ∼80% at baseline.

Description of the CIQ instrument

The CIQ included sociodemographic characteristics (birth date, sex, marital status, and years of education). A “food getting frequency” recorded the number of times over the past 30 days that the respondent “got” various food items for the entire household. Thirty-nine different foods were included, focusing on foods to be promoted by the HFH intervention, and their less healthy alternatives.

The CIQ then asked the caregiver respondent to describe household meal patterns and child feeding. Questions addressed caregiver's psychosocial factors, including food-related self-efficacy, intentions, and knowledge. The self-efficacy section asked the respondent to describe their confidence in making healthy food selections, using preparation methods promoted by the intervention, and using food labels to make healthy choices when selecting foods. The food intentions section addressed the respondent's intention to purchase, consume, and prepare foods promoted by the intervention. The food-related knowledge section asked questions that related to nutrition information provided by the intervention. A section on household socioeconomic status assessed employment, participation in food assistance programs, food security, and ownership of material goods. The final section assessed general patterns of food consumption through a qualitative food frequency instrument of 39 foods.

Description of the CCIQ

A brief questionnaire was administered with the focal child, which included questions on the child's frequency of participation in selection and preparation of family meals, healthy food choice intentions, healthy food identification, health knowledge, and a qualitative food frequency that included 14 foods. The ordering of sections in both the CIQ and CCIQ was designed to minimize biasing effects of earlier sections on later sections. Respondents were asked first what they did (behavior) and later what they felt were the right choices to make (knowledge).

Dietary recalls

A single 24-h dietary recall was collected on each of the adult caregiver and child pair, both pre- and postintervention. A modified US Department of Agriculture's five-step multiple pass methodology was used. To decrease the length of the food recall, time and occasion of food and beverage consumption were collected in combination with either the first or third step, yielding a four-step method. A simple set of food models was used for visual aid and a list of commonly “forgotten foods” was used as a prompt during the last stage of the recall. One sixth of recalls were collected on Mondays, to include weekend intake. All other recalls were collected on weekdays. Dietary data were analyzed using the Cancer Research Center of Hawaii's food composition database, which contains local foods and recipes not found in the US Department of Agriculture standard reference.

Training of data collectors

Data collectors on Oahu were primarily health center staff, University students, and HFH project staff. On the Island of Hawaii, HFH project staff conducted training of community members who carried out the data collection. Training took place >3 days and included demonstration, role play, observation of pilot interviews, and supervised initial interviews.

Scale and score construction

A series of additive scores and scales were developed to measure the main psychosocial and behavioral constructs in the conceptual framework, based on Social Cognitive Theory (SCT) and the Theory of Planned Behavior (28). All scales were assessed for internal reliability using the Cronbach's α statistic. Similar scales have been developed for our other food store intervention trials and have been shown to be significantly related to study outcomes (15,23,24,25,26,27).

Adult caregiver scales

Caregiver food knowledge (behavioral capability, SCT) scale is the sum of scores from six multiple-choice questions that asked respondents to identify a food lowest in fat, highest in fiber, or lowest in total calories, or the cooking method that would result in the least the amount of fat. Scores had a possible and actual range from 0 to 6 with a mean of 4.3 (s.d. = 1.4, α = 0.58).

Caregiver food self-efficacy (SCT) scale is based on nine statements about healthy food purchasing, preparation, and consumption. Respondents were asked how easily they could do a particular behavior regularly. Scores had a possible range of 9–36, with an actual range from 16 to 36, and a mean of 30.3 (s.d. = 4.6, α = 0.65).

Caregiver food intentions (Theory of Planned Behavior) scale is based on seven questions where the respondent was asked to state their intention for future food choices. A higher score was given for the choice that reflected the lowest fat or lowest sugar choice. Scores had a possible range of 0–18 with an actual range of 7–18 and a mean of 10.8 (s.d. = 3.5, α = 0.61).

Healthy food getting frequency scale includes 16 different foods (and food groups) that were promoted as part of the HFH program (e.g., baked chips, water). Adult caregivers were asked to recall the number of times they got each food in the previous 30 days. Scores had a possible range of 0–480, with an actual range of 5–377, and a mean of 44.2 (s.d. = 45.9, α = 0.91).

Unhealthy food getting frequency scale includes nine different foods that were demarketed as part of the HFH program (e.g., regular chips). Caregiver respondents were asked to recall the number of times they got each food in the previous 30 days. Total unhealthy food getting frequency scores had a possible range of 0–270, with an actual range of 0–260, and a mean of 16.7 (s.d. = 23.3, α = 0.90).

Healthy food convenience perception scale includes three questions on the perceived convenience of selecting, preparing, and serving healthy food options. Caregivers were asked to provide their level of agreement with a series of statements (e.g., healthy food is inconvenient to prepare), with a higher score indicating greater perceived convenience of healthy foods. Scores had a possible and actual range of −3 to 3, and a mean of +1.9 (s.d. = 1.7, α = 0.75).

Caregiver healthy food consumption scale includes previous 30-day consumption frequency for 13 different low-sugar and/or high-fiber foods that were promoted as part of the HFH program. Respondents were provided with eight frequency categories ranging from “never in past 30 days” to “two times a day or more,” which were converted into number of times a month. Scores had a possible range of 0–780, with an actual range of 0–390, and a mean of 64.6 (s.d. = 67.6, α = 0.77).

Material style of life (environment, SCT) scale was developed as a proxy for socioeconomic status. Respondents were asked to list the number of seven household items they owned in working condition (e.g., computer). Scores had a possible and actual range from 1 to 7 with a mean of 4.7 (s.d. = 1.1, α = 0.51).

Child scales

Child healthy food knowledge identification scale assessed the focal child's ability to identify which of two foods is healthier (e.g., frosted flakes vs. Cheerios). Seven questions were included in the final scale, with correct answers each given one point. Scores had an possible and actual range from 0 to 7 with a mean of 5.0 (s.d. = 1.7, α = 0.61).

Child healthy food intentions assessed the focal child's predicted choice among three different foods. If the lower fat, lower sugar, and/or higher fiber option was chosen, the child received a point. Scores had a possible and actual range from 0 to 12 with a mean of 7.5 (s.d. = 3.1, α = 0.60).

Child healthy food consumption scale includes previous 7-day consumption frequency responses for eight different low-sugar, low-fat, and/or high-fiber foods promoted as part of the HFH program. Respondents were provided with four frequency categories ranging from “never” to “every day” which were converted into number of times a week. Scores had a possible range from 0 to 56, with an actual range from 4 to 51, and a mean of 27.4 (s.d. = 9.5, α = 0.66).

Child unhealthy food consumption scale includes previous 7-day consumption frequency responses for eight different foods that were high in sugar, fat, and/or lower in fiber, which were demarketed by the HFH program. Scores had a possible range from 0 to 56, with an actual range from 0 to 26, and a mean of 10.2 (s.d. = 6.1, α = 0.55).

Healthy Eating Index (HEI) component scores were calculated, each of which could range from 0 to 9 (29). A total HEI score was determined as the sum of the nine components; the dietary variety component was excluded because an appropriate database was not available to analyze the data. Although the Food Guide Pyramid food groups have now been revised to correspond with MyPyramid (30), and a new method of calculating the HEI has been released (31), these databases were not available for this study.

Process evaluation

A detailed process evaluation was conducted on implementation of the HFH program, including observation of store availability of promoted foods, presence of program signage, and record of consumer contacts during interactive sessions.

Data analysis

Prior to initiating the trial, we calculated the sample size needed for detecting meaningful differences in four variables: HEI score, percent of energy from fat, number of servings of vegetables, and number of servings of fruit. Using a two-sided paired t-test, a significance level of 5% and a power of 90%, we found that a sample size of 130 child–caregiver pairs would be required. Of the original 184 households sampled, 10 were eliminated due to missing data, yielding a final sample of 174 caregiver–child pairs at baseline. Postintervention, we attempted to interview as many of the baseline respondents as possible, and were able to interview 116, a 67% retention rate. The sample for whom we had pre- and postintervention data differed significantly from those for whom we were unable to get postintervention data, in terms of percent female (99.2 and 89.2%, respectively) and proportion employed (52 and 49%, respectively). No other differences in pre- and postintervention sociodemographic data were observed.

Multivariable linear regressions were conducted for each of the psychosocial and behavioral outcome, including HEI indicators and gram consumption of promoted foods to assess the programs' impact. We calculated the difference between baseline and postintervention scores for the caregiver and child psychosocial and behavioral variables, and then regressed the result on a series of independent variables, including intervention group assignment, caregiver age, sex, years of education, employment status, time period between baseline and postintervention measurements, caregiver ethnicity, and material style of life score.

We examined change in intake of the key targeted foods (i.e., water, 100% juice, diet soda, 2% milk, 1% milk, low-sugar cereals, local fruits, and vegetables) from pre- to postintervention. Due to low consumption of these foods (≥75% of respondents never consumed a particular targeted food, with water the exception), we treated consumption of the targeted foods as dichotomous (any nonzero value = 1, else 0). McNemar's paired tests were performed where each participant's final response was compared to their initial response to assess change in consumption of targeted foods. Data were analyzed using SAS 9.1 (SAS Institute, Cary, NC). We checked the distribution of the dependent variables and log transformation was conducted for variables that were not normally distributed. The variety of outcome variables being used increases the probability of type 1 errors. To obtain a global assessment of the impact (if any) of the intervention, multivariate tests for the effect of intervention on the entire set of change in quantitative outcome variables, adjusted for covariates, were used for the set of 20 caregiver variables and the set of 16 child variables using the “mtest” option in SAS.

Human subjects approval for this study was given by the University of Hawaii Committee on the Use of Human Subjects. Signed informed consent was obtained from all adult and child respondents.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Disclosure
  9. REFERENCES

Demographic characteristics of caregivers and focal children

At baseline, the majority of the respondents were self-identified as Native Hawaiian or other Pacific Islander (64%). The majority of caregivers were female (95%), with a mean age of 41 years (Table 1). Caregivers' level of schooling varied, with a mean of 12.5 years. The unemployment rate among respondents was over 34% (n = 69) (self-identified as “student” or “not employed”). Of these, 6 respondents identified themselves as students and 63 said they were not employed. Most households (67%) received at least one form of food assistance. The mean age of focal children was 9.9 years; 50% of them were girls. No significant differences with respect to demographic characteristics were observed between the intervention and comparison samples, with the exception that there were fewer caregivers in the intervention sample who had worked >40 h a week and they were more likely to be unemployed. Thus, employment was controlled in analytic models.

Table 1.  Sociodemographics of the Healthy Foods Hawaii study sample
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Program implementation

HFH process data indicate that the program was implemented with high fidelity and moderate reach and dose. Participating food stores stocked promoted foods at very high rates and point-of-purchase signage was readily available. Forty in-store interactive sessions were held, attaining 1,150 consumer contacts. Stores reported increased sales of several promoted foods, including low sodium luncheon meats, light mayonnaise, and reduced fat potato chips. On the other hand, self-reported exposure to intervention materials by our intervention study sample was low to moderate.

Impact of the HFH intervention on caregiver food-related psychosocial factors, behaviors, and dietary intake

There was a statistically significant improvement in food-related knowledge among adult caregivers in the intervention sample, and this improvement was maintained in multivariate regression analyses after adjustment for caregiver age, sex, education, employment, time period between pre- and postmeasurements, and other demographic factors (Table 2). There was a borderline significant improvement in the perception that healthy foods are convenient among adult caregivers in the intervention sample, and this improvement achieved statistical significance in multivariate regression analyses after adjustment for caregiver age, sex, education, employment, time period between pre- and postmeasurements, and other demographic factors (unadjusted P = 0.0970; adjusted P = 0.0171). Significant impacts were not observed in terms of other caregiver psychosocial factors (e.g., self-efficacy, intentions, child feeding responsibility), behaviors (frequency of unhealthy food purchasing, frequency of caregiver healthy food consumption), or in HEI components or targeted food consumption for caregivers (data not shown for targeted food consumption other than water consumption). The global multivariate test for the effect of intervention using all 20 dependent variables, adjusted for covariates, was highly significant (P = 0.004).

Table 2.  Impact of the HFH program on caregivers (β and s.e. of the intervention group, multiple regression models)
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Impact of the HFH intervention on child food-related psychosocial factors, behaviors, and dietary intake

No significant changes were observed in children's psychosocial variables (child health knowledge, child food knowledge, intentions) by treatment group. Improvement in intervention children's healthy food intention scores was not maintained after adjustment for covariates. After adjustment, intervention children showed significantly increased total HEI scores, increased HEI grain scores, and total water consumption as compared to comparison children (Table 3). No impact was shown in the gram consumption of any of the other targeted foods (data not shown). No other significant behavioral impacts were observed for children (e.g., child frequency of healthy food consumption, child frequency of unhealthy food consumption). The multivariate test for the effect of intervention on change in the set of 16 dependent variables, adjusted for covariates, was significant (P = 0.02).

Table 3.  Impact of the HFH program on children (β and s.e. of intervention group, multiple regression models)
inline image

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Disclosure
  9. REFERENCES

This article reports on the results of one of the first food store intervention trials to target both caregivers and their children who are Native Hawaiians and Pacific Islanders. A unique aspect of HFH was the involvement of local producers and distributors as part of the program. Involving multiple levels of the food system is important for longer-term sustainability of intervention activity and its effects (32).

We found positive impacts of the intervention on improved adult caregiver psychosocial factors (healthy food knowledge), child total HEI scores, and water consumption. These results are similar to our findings from other programs that our team has implemented in food stores serving low-income ethnic minority communities. On two Apache reservations we found significant impacts of the intervention on knowledge, healthy food purchasing frequency, and gram consumption of promoted foods (23). Among First Nations we were able to see impacts on knowledge and healthy food purchasing frequency (24). In a different urban Pacific Islander population, we were also able to show impacts on food purchasing and knowledge (25).

Although the foods and strategies promoted in the intervention were selected to impact childhood obesity (e.g., drinking water rather than soda) and changes in children's dietary habits, many of the messages were targeted toward influencing the adult caregiver, which may account for the greater impact of the intervention on caregivers as compared to children, in terms of psychosocial outcomes. The intervention materials were not specifically designed for a child audience. Nevertheless, we were pleased to see significant impacts of the program in terms of several key outcomes in children, including overall HEI score, HEI grain score, and water consumption. Increased water consumption, in particular, may contribute to obesity prevention, as it likely substitutes for more calorically dense beverages (33). Our grain measure did not distinguish between whole and other grains, which would be valuable to do in a future study. Although multivariate analyses indicated that children in the intervention group may have increased their energy intakes relative to children in the control group, the changes were highly variable, and the difference was not statistically significant. We collected only one dietary recall for each child and caregiver before and after the intervention, which permitted an examination of changes in mean intakes, but precluded analyses of changes by individuals. In addition, future interventions should explore the impact of materials designed to be more developmentally targeted for the child audience, with supporting information for caregivers. Due to a lower than expected retention rate, from pre- to postintervention (67%), we did not achieve our planned sample size of 130 respondents. The fact that we were able to see significant impact in terms of overall HEI score, and with significant m-tests for overall effects with multiple outcomes, indicates the robustness of our findings.

Self-reported exposure to intervention materials by our intervention sample was low to moderate, and may also have accounted for the modest effect sizes observed. Low exposure to posted materials and interactive sessions in stores is not unexpected when the intervention took place in a small number of food stores in a setting with many food sources, and particularly in communities where members commute large distances to urban areas to purchase foods from wholesale stores. Future food store intervention trials should increase their reach to a greater proportion of local food sources in order to see stronger effects, and should expand their efforts to include community settings outside of stores. A more intense program of longer duration, supported by policy changes, would likely have broader impacts in terms of child healthy food behavior.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Disclosure
  9. REFERENCES

We acknowledge the support and collaboration provided by the staff of Waimanalo Health Center, Waianae Coast Comprehensive Health Center, SET IT UP, Kau Rural Health Community Association, and members of the communities of Wai'anae, Waimanalo, North Kohala, and Kau. The local stores and producers in the communities of Waianae and North Kohala, and the individual distributors were also essential contributors and key partners in the implementation of the HFH program. This work was funded by the US Department of Agriculture grant 2004-35215-14252 and Hawaii Department of Health grant #436851.

REFERENCES

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Disclosure
  9. REFERENCES
  • 1
    Flegal KM. Epidemiologic aspects of overweight and obesity in the United States. Physiol Behav 2005; 86: 599602.
  • 2
    Flegal KM, Carroll MD, Ogden CL, Johnson CL. Prevalence and trends in obesity among US adults, 1999–2000. JAMA 2002; 288: 17231727.
  • 3
    Hedley AA, Ogden CL, Johnson CL et al. Prevalence of overweight and obesity among US children, adolescents, and adults, 1999–2002. JAMA 2004; 291: 28472850.
  • 4
    Ogden CL, Carroll MD, Curtin LR et al. Prevalence of overweight and obesity in the United States, 1999–2004. JAMA 2006; 295: 15491555.
  • 5
    Ogden CL, Carroll MD, Flegal KM. High body mass index for age among US children and adolescents, 2003–2006. JAMA 2008; 299: 24012405.
  • 6
    Coyne T. Lifestyle Diseases in the Pacific Islands. SPC Technical Paper. Secretariat of the Pacific Commission: Noumea, New Caledonia, 2000.
  • 7
    McGarvey ST. Obesity in Samoans and a perspective on its etiology in Polynesians. Am J Clin Nutr 1991; 53: 1586S1594S.
  • 8
    Davis J, Busch J, Hammatt Z et al. The relationship between ethnicity and obesity in Asian and Pacific Islander populations: a literature review. Ethn Dis 2004; 14: 111118.
  • 9
    Ochner MH, Salvail FR, Ford ES, Ajani U. Obesity and self-reported general health, Hawaii BRFSS: are Polynesians at higher risk? Obesity (Silver Spring) 2008; 16: 923926.
  • 10
    Kemmer TM, Novotny R, Gerber AS, Ah I Ping. Anemia and growth patterns in children ages 5 to 10 years living in American Samoa. Pub Health Nutr J 2008; 12: 17.
  • 11
    Kemmer TM, Novotny R, Ah I Ping. Iron deficiency and anemia: disparity exists between children in American Samoa and children living within the US. Eur J Clin Nutr 2008; 62: 754760.
  • 12
    Rodriguez BL. Dietary studies in the multi-ethnic Hawaiian population. J Am Diet Assoc 2006; 106: 209210.
  • 13
    Lee SK, Novotny R, Daida YG, Vijayadeva V, Gittelsohn J. Dietary patterns of adolescent girls in Hawaii over a 2-year period. J Am Diet Assoc 2007; 107: 956961.
  • 14
    Smith BJ, Phongsavan P, Havea D, Halavatau V, Chey T. Body mass index, physical activity and dietary behaviours among adolescents in the Kingdom of Tonga. Public Health Nutr 2007; 10: 137144.
  • 15
    Gittelsohn J, Haberle H, Vastine AE, Dyckman W, Palafox NA. Macro- and microlevel processes affect food choice and nutritional status in the Republic of the Marshall Islands. J Nutr 2003; 133: 310S313S.
  • 16
    Daida Y, Novotny R, Grove JS, Acharya S, Vogt TM. Ethnicity and nutrition of adolescent girls in Hawaii. J Am Diet Assoc 2006; 106: 221226.
  • 17
    Seymour JD, Yaroch AL, Serdula M, Blanck HM, Khan LK. Impact of nutrition environmental interventions on point-of-purchase behavior in adults: a review. Prev Med 2004; 39 (Suppl 2): S108S136.
  • 18
    Morland K, Wing S, Diez A Roux, Poole C. Neighborhood characteristics associated with the location of food stores and food service places. Am J Prev Med 2002; 22: 2329.
  • 19
    Morland K, Diez AV Roux, Wing S. Supermarkets, other food stores, and obesity: the atherosclerosis risk in communities study. Am J Prev Med 2006; 30: 333339.
  • 20
    Morland KB, Evenson KR. Obesity prevalence and the local food environment. Health Place 2009; 15: 491495.
  • 21
    Franco M, Diez-Roux AV, Nettleton JA et al. Availability of healthy foods and dietary patterns: the Multi-Ethnic Study of Atherosclerosis. Am J Clin Nutr 2009; 89: 897904.
  • 22
    Bodor JN, Rose D, Farley TA, Swalm C, Scott SK. Neighbourhood fruit and vegetable availability and consumption: the role of small food stores in an urban environment. Public Health Nutr 2008; 11: 413420.
  • 23
    Gittelsohn J, Anliker JA, Ethelbah B et al. A food store intervention to reduce obesity in two American Indian communities: impact on food choices and psychosocial indicators. FASEB J 2005; 19: A594A597.
  • 24
    Ho LS, Gittelsohn J, Rimal R et al. An integrated multi-institutional diabetes prevention program improves knowledge and healthy food acquisition in northwestern Ontario First Nations. Health Educ Behav 2008; 35: 561573.
  • 25
    Gittelsohn J, Dyckman W, Frick KD et al. A pilot food store intervention in the Republic of the Marshall Islands. Pac Health Dialog 2007; 14: 4353.
  • 26
    Gittelsohn J, Anliker JA, Sharma S et al. Psychosocial determinants of food purchasing and preparation in American Indian households. J Nutr Educ Behav 2006; 38: 163168.
  • 27
    Ho L, Gittelsohn J, Sharma S et al. Food-related behavior, physical activity, and dietary intake in First Nations - a population at high risk for diabetes. Ethn Health 2008; 13: 335349.
  • 28
    Rajgopal R, Cox RH, Lambur M, Lewis EC. Cost-benefit analysis indicates the positive economic benefits of the Expanded Food and Nutrition Education Program related to chronic disease prevention. J Nutr Educ Behav 2002; 34: 2637.
  • 29
    Basiotis PP, Carlson A, Gerrior SA, Lino M. The Healthy Eating Index: 1999–2000. Department of Agriculture, Center for Nutrition Policy and Promotion, 2002.
  • 30
    MyPyramid. US Department of Agriculture, Center of Nutrition Policy and Promotion, 2005.
  • 31
    Guenther PM, Reedy J, Krebs-Smith SM, Reev BB, Basiotis PP. Development and Evaluation of the Healthy Eating Index—2005. Technical Report. Center for Nutrition Policy and Promotion, US Department of Agriculture, 2007.
  • 32
    Sallis JF, Glanz K. Physical activity and food environments: solutions to the obesity epidemic. Milbank Q 2009; 87: 123154.
  • 33
    Wang YC, Ludwig DS, Sonneville K, Gortmaker SL. Impact of change in sweetened caloric beverage consumption on energy intake among children and adolescents. Arch Pediatr Adolesc Med 2009; 163: 336343.