Nutrition Knowledge, Food Label Use, and Food Intake Patterns among Latinas with and without Type 2 Diabetes

RESEARCH Current Research

Nutrition Knowledge, Food Label Use, and Food Intake Patterns among Latinas with and without Type 2 Diabetes NURGÜL FITZGERALD, PhD, RD; GRACE DAMIO, MS; SOFIA SEGURA-PÉREZ, MS, RD; RAFAEL PÉREZ-ESCAMILLA, PhD

ABSTRACT Objective To examine the associations of nutrition knowledge, food label use, and food intake patterns among Latinas with and without diagnosed diabetes. Design This was a case-control study. Subjects/setting A convenience sample of 201 (100 cases with diagnosed type 2 diabetes, 101 controls without diagnosed diabetes) nonpregnant, nonbreastfeeding Latinas without severe health conditions, aged 35 to 60 years were interviewed by bicultural interviewers. Diverse community-based recruitment methods were used. Statistical analyses performed Independent samples t test, Mann-Whitney U, and ␹2 tests, and multivariate logistic regression were performed. Results Food labels self-efficacy and stage of change, and average nutrition knowledge scores were similar between cases and controls (P⬎0.05). Within the diabetes group, nutrition knowledge was greater among those who had seen a registered dietitian or a diabetes educator (P⫽0.020). Cases reported consuming artificially sweetened desserts and beverages more frequently than controls (P⬍0.001). Pooled sample cross-sectional analyses showed that nutriN. Fitzgerald is an assistant professor, Family and Community Health Sciences Department and Department of Nutritional Sciences, Rutgers, The State University of New Jersey, New Brunswick; at the time of the study, she was a graduate research assistant, Department of Nutritional Sciences, University of Connecticut, Storrs. G. Damio is director of the Center for Community Nutrition and the Center for Women and Children’s Health at Hispanic Health Council, Hartford, CT. S. Segura-Pérez is associate director, Center for Community Nutrition, Hispanic Health Council, Hartford, CT. R. Pérez-Escamilla is professor and director of Latino Health Disparities NIH EXPORT Center, Department of Nutritional Sciences, University of Connecticut, Storrs. Address correspondence to: Nurgül Fitzgerald, PhD, RD, Family and Community Health Sciences Department and Department of Nutritional Sciences, Rutgers, The State University of New Jersey, 26 Nichol Avenue, New Brunswick, NJ 08901. E-mail: nfitzgerald@rce. rutgers.edu Manuscript accepted: December 28, 2007. Copyright © 2008 by the American Dietetic Association. 0002-8223/08/10806-0004$34.00/0 doi: 10.1016/j.jada.2008.03.016

960

Journal of the AMERICAN DIETETIC ASSOCIATION

tion knowledge was positively related to food label use, which in turn was related to a more healthful food intake pattern (P⬍0.05). After adjusting for likely confounders, socioeconomic status (SES) was positively related to nutrition knowledge (P⫽0.001) and intakes of fruits, vegetables, and meats (Pⱕ0.01). SES was not related to food label use independently of nutrition knowledge. Acculturation was positively related to soft drink and salty snack intakes (P⬍0.01). Conclusions There is a need to improve nutrition knowledge and skills for both groups, especially for those with low SES. Culturally appropriate interventions should emphasize the healthful nutritional behaviors from one’s primary culture for effective retention of such traits. J Am Diet Assoc. 2008;108:960-967.

T

ype 2 diabetes prevalence continues to increase, and Hispanics or Latinos are almost twice as likely to have type 2 diabetes compared to non-Hispanic whites (1). Among the multitude of risk factors for this disease, poor dietary intake (2), particularly a dietary pattern characterized by greater intakes of sugar-sweetened drinks (3-5), red or processed meats, refined grains, snacks, sweets, and desserts, has been associated with a higher risk of type 2 diabetes (5). On the other hand, a pattern including greater intakes of fruits and vegetables is negatively related to energy density, obesity (6), and diabetes risk (7,8). Other factors, such as socioeconomic status (SES) and acculturation, also contribute to type 2 diabetes etiology. Low SES is related to a higher prevalence of type 2 diabetes (9) and lower intakes of fruits and vegetables (10,11). Less-acculturated Mexican Americans are likely to have more healthful diet profiles compared to highly acculturated Mexican Americans (12-14). Lifestyle modification, including an optimal diet, is an effective prevention method for type 2 diabetes (15), and nutrition-related knowledge and skills, such as using the Nutrition Facts panel (food label) can help improve dietary intake patterns. Research shows that food label use is positively related to nutrition knowledge (16,17) and fruit and vegetable intakes (18) and negatively related to fat intake (19). Hispanics are reported to use food labels less than non-Hispanic whites (20), but to the authors’ knowledge, the associations of nutrition knowledge, food label use, and dietary intake among Latinos with and without type 2 diabetes, especially among subpopulations other than Mexican Americans, have not been reported.

© 2008 by the American Dietetic Association

In this predominantly Puerto Rican sample, we hypothesized that greater nutrition knowledge and food label use would be related to more healthful food intake patterns, and that Latinas with diagnosed type 2 diabetes would have better nutrition knowledge, greater food label use, and more healthful food intake patterns than Latinas without diagnosed diabetes. SES, acculturation status, and psychosocial factors were also examined in this context. METHODS Design and Sample This study was conducted in a convenience sample of 201 Latinas (100 cases with self-reported diagnosed type 2 diabetes [Diabetes group] and 101 controls without selfreported diabetes [Control group]), living in Hartford, Connecticut. Participants were nonpregnant, nonbreastfeeding, 35- to 60-year-old Latinas without severe health conditions (ie, self-reported cancer, acquired immunodeficiency syndrome, kidney failure). A variety of recruitment methods (street outreach: 32.0%; telephone directory: 20.5%; Hispanic Health Council programs: 14.0%; previous study participant lists: 12.5%; health fairs/bulletin boards: 7.5%; schools, health clinics, word of mouth: 13.5%) were utilized to capture the diversity of the target community. Interviews were conducted between June 2002 and September 2003 by two trained bicultural interviewers at participants’ homes (95.5%), Hispanic Health Council (2.5%), or other locations (2.0%) chosen by participants. Interviews, which took approximately 1 hour to complete, were conducted in the language of each participant’s choice (86.1% in Spanish, 8.0% in English, 6.0% in both). Upon completion, participants were provided with a nutrition information packet. The study was approved by the Human Subjects Review Committees of the University of Connecticut and the Hispanic Health Council. Measures The study questionnaire (21) was developed from instruments designed for this community (22-24) and others (25-29). It was translated and back-translated to and from Spanish (by authors R.P.-E. and S.S.-P.), reviewed by two registered dietitians (RDs) for content validity (N.F. and S.S.-P.), and pretested in the target community for face validity, which resulted in minor changes. Data were entered manually and duplicate entry was performed on 10% of the sample as a measure of quality control. Participants were classified as having diabetes if they answered “Yes” to a question, which was adopted from commonly used national surveys, such as the National Health and Nutrition Examination Survey (30), “Has a doctor ever told you that you have diabetes?” Previous studies support the high reliability and (␬⫽0.79, sensitivity⫽75%, specificity⫽ 98%) (31) and validity (91% to 99% confirmed cases) (32) of self-reported diagnosed diabetes using this approach. Participants were assigned to the Diabetes group after ensuring their type 2 diabetes status through additional questions, such as if they have not used insulin for as long as they had diabetes, the name/location of the diagnosing doctor; the time (year) of diagnosis; and the oral glycemic medication use. Differently than the Control group, the

Diabetes group was asked additional diabetes care-related questions, such as if they had ever received services from an RD or a diabetes educator and when they had seen each professional. Latinas who reported not having diabetes at the time of the screening were assigned to the Control group. Herman and colleagues’ (33) risk assessment questionnaire was used to determine the Control group participants who were at high risk for having undiagnosed diabetes. This questionnaire, which is based on age, physical activity, obesity status, history of diabetes in the family, and delivery of a macrosomic infant, is reported to perform better than the American Diabetes Association’s risk questionnaire, and it had 80% sensitivity and 61% specificity in a nationally representative sample of minorities, including Hispanics. For this assessment, being physically active was defined as any planned physical activity, such as brisk walking, aerobics, jogging, that is done three to five times a week for at least 20 to 60 minutes per day. Body mass index (BMI) was calculated from weight and height, which were measured twice by the interviewers to the nearest 0.25 lb and 1/8 inch, respectively, using a portable scale (Salter, Oak Brook, IL) and a stadiometer (Seca, Hamburg, Germany) following standard procedures (34). Measurements were taken with the participants wearing light clothing and no shoes. The 25-item nutrition knowledge scale (Cronbach’s ␣⫽.813) was adapted from instruments previously used in this community (24) and others (25,26). Knowledge scale questions included recognizing the given definitions of the Food Guide Pyramid (35) and fiber, identifying the recommended number of servings for the Food Guide Pyramid food groups (six items), common sources of fat, saturated fat, cholesterol, and carbohydrates, and relationships of health with dietary fat and fiber. Correct answers were assigned a ⫹1, and incorrect answers were assigned a 0. The nutrition knowledge score (range⫽2 to 20; median⫽10) was used as a dichotomized variable (less than or equal to median and greater than median) in the multivariate analyses. Overall food label use was determined by asking, “How often do you use food labels to select foods that are better for your health?” Using specific sections of food labels was determined by asking, “How often do you look at food labels to select foods that are . . . (low in fat/saturated fat/cholesterol/sugar/salt or sodium/high in fiber)?” and “How often do you look at . . . (serving size/total carbohydrates/calories/ingredient list/nutrient claims) on a food label?” The food label responses were dichotomized into users (use it often) and nonusers (use it sometimes/do not use it/not familiar with food labels). The Transtheoretical Model (stages of change) (36), and Social Cognitive Theory (self-efficacy) (37) were used to examine psychosocial factors related to food-intake patterns. Stages of change questions for using food labels, decreasing animal fat and salt, and increasing fiber in the diet were adapted from a valid instrument (27). For analytical purposes, precontemplation, contemplation, and preparation stages were combined into a pre-action stage. The action stage (action and maintenance) included those who reported practicing the relevant behavior with at least some success for the past 6 months. Self-efficacy for using food labels to select healthful foods was adapted from a validated questionnaire (28) and the answers were

June 2008 ● Journal of the AMERICAN DIETETIC ASSOCIATION

961

grouped into confident (very confident/confident/somewhat confident) and not confident (not confident) categories. Usual dietary intake was assessed by an 18-item food frequency questionnaire, which was adapted from an instrument that had been shown to be reliable in the target community (29). For the current analyses, foods were grouped into fruits and vegetables (fruits, 100% fruit juice, starchy/root/green leafy/other vegetables), meats (beef, pork, lamb, chicken, eggs, fish, shellfish), grains (pasta, cereal, bread, other grains), dairy (milk, cheese, yogurt), legumes, regular soft drinks (soft and juice drinks made with regular sugar), diet soft drinks (soft and juice drinks made with artificial sweeteners), sweets (sweets and desserts made with regular sugar), sweets made with artificial sweeteners, and salty snacks (eg, potato or tortilla chips). Education (low: less than high school diploma; high: high school diploma/general educational development [GED] or higher) was used as an indicator of SES. Employment status (employed: worked full- or part-time; unemployed: unemployed/homemaker/student/retired/disabled), current Food Stamp Program participation (yes or no), and marital status (have partner: married/not married but have a partner; no partner: single/divorced/widowed and no partner) were also recorded. Food insecurity was measured by the 6-item United States Department of Agriculture Household Food Security Module (38). Education was selected as the main SES indicator because of its very common use in health outcomes research (39) and to avoid the reverse causality bias that would be likely if the other available SES indicators were used. A six-item acculturation scale (Cronbach’s ␣⫽.726) (21) was adapted from a comprehensive, multidimensional instrument, which was developed for this population, had an adequate internal consistency, and correlated well with commonly used acculturation variables (22,40). The sixitem scale included self-identification, bilingual status, preferred language at home, city size, and country/territory where the respondent grew up, and length of residence in the United States (split by a median of 20 years). The acculturation score was calculated by assigning each dichotomous item a 0 for a lower level and a ⫹1 for a higher level of acculturation (range⫽0 to 6, median: 1), and it was dichotomized into less (ⱕ1) and highly (⬎1) acculturated. Statistical Analyses Sample size estimations indicated that there was adequate power to detect group differences in food intake frequencies. For example, 34 people were needed in each comparison group to detect one count (1.00) difference in the daily intake frequency of vegetables by using an ␣ level of .05, a variance of 1.08, and 80% power. Whereas some analyses were performed following the case-control study design, others were conducted by pooling both groups and applying a cross-sectional analysis strategy. Descriptive statistics, bivariate (␹2, independent samples t and Mann-Whitney U tests) and multivariate analyses were conducted using SPSS for Windows 12.0 (2003, SPSS Inc, Chicago, IL). All logistic regression models were adjusted for age, BMI, and group assignment. A two-tailed P value of ⬍0.05 or 95% confidence interval (CI) of odds ratio (OR) that was exclusive of the

962

June 2008 Volume 108 Number 6

value of 1.0 was used as the criterion for statistical significance. The goodness-of-fit of the models were examined with the Hosmer and Lemeshow test and considered adequate at a P value of ⱖ0.05. RESULTS Diabetes and Control Group Differences The Diabetes group was about 3 years older and less acculturated than the Control, but they were similar in other sociodemographic characteristics (Table 1). The risk-assessment questions indicated that 60.4% of the Control group was at high risk for having undiagnosed diabetes. The nutrition knowledge score was not significantly different between the two groups. However, in the Diabetes group, Latinas who had ever seen an RD or a diabetes educator (n⫽64) were more likely to have a higher nutrition knowledge score compared to Latinas who had not seen one (11.11⫾4.28 vs 9.00⫾4.32, respectively; P⫽0.020), despite the fact that there were no differences in their SES, health insurance, and acculturation status. Stages of change and self-efficacy for using food labels, and reported food label use were similar between groups, with the exception of using food labels to select foods low in sugar (67.5% Diabetes group vs 34.1% Control group; P⬍0.001). The Diabetes group reported a significantly lower intake frequency of sweets (0.29⫾0.74 Diabetes group, 0.44⫾0.61 Control group; P⬍0.001) and regular soft drinks (1.12⫾1.77 Diabetes, 2.15⫾2.32 Control; P⬍0.001) and a higher intake of artificially sweetened versions of these foods (0.21⫾0.60 Diabetes, 0.05⫾0.35 Control; P⬍0.001) and drinks (0.49⫾0.94 Diabetes, 0.17⫾0.62 Control; P⬍0.001). After adjusting for acculturation, education, age, BMI, nutrition knowledge, and food label use, the Diabetes group was less likely to consume regular soft drinks and sweets (Table 2) and more likely to consume artificially sweetened beverages (OR: 7.34; 95% CI: 3.48 to 15.50; P⬍0.001) and sweets (OR: 6.96; 95% CI: 2.80 to 17.32; P⬍0.001) frequently (greater than median) compared to Controls. The Diabetes group reported consuming meats less frequently (1.20⫾0.69 Diabetes, 1.43⫾0.77 Control; P⫽0.032), but the difference was not statistically significant after adjusting for likely confounders. Other food group intakes were similar between the Diabetes and Control; pooled average daily intake frequencies were 0.63⫾0.47 for legumes, 1.84⫾1.66 for dairy, 2.22⫾1.19 for grains, 0.22⫾0.49 for salty snacks, and 3.60⫾2.16 for fruits and vegetables. About 26.9% of the fruit and vegetable intake came from 100% fruit juices. POOLED CROSS-SECTIONAL ANALYSES Nutrition Knowledge About 35.8% of the participants had not heard about the Food Guide Pyramid (35) and another 10.5% had heard about it, but they could not correctly identify the definition of the Food Guide Pyramid in a multiple choice question format. Among those who were familiar with the Food Guide Pyramid (64.2%), large proportions did not know the recommended servings of the Food Guide Pyr-

Table 1. Characteristics of Latinas with and without type 2 diabetes in Connecticut All (nⴝ201)

Education Low (⬍high school diploma) High (high school diploma or higher) Employment Employed Unemployed Marital status Have partner No partner Current Food Stamp Program participation Yes No Self-identified ethnicitya Puerto Rican Other Latino Acculturationb Less acculturated (ⱕmedian) Highly acculturated (⬎median) Nutrition knowledge scorec ⱕMedian ⬎Median Mean⫾SDd BMI,e mean⫾SD Age (y), mean⫾SD

Control (nⴝ101)

Diabetes (nⴝ100)

n

%

n

%

n

%

128 73

63.7 36.3

62 39

61.4 38.6

66 34

66.0 34.0

57 144

28.4 71.6

31 70

30.7 69.3

26 74

26.0 74.0

81 120

40.3 59.7

41 60

40.6 59.4

40 60

40.0 60.0

97 104

48.3 51.7

46 55

45.5 54.5

51 49

51.0 49.0

170 31

84.6 15.4

85 16

84.1 15.9

85 15

85.0 15.0

106 95

52.7 47.3

46 55

45.5 54.5

60 40

60.0* 40.0

109 92

54.2 45.8 10.45⫾4.49 32.93⫾8.55 48.94⫾6.60

54 47

53.5 46.5 10.54⫾4.61 31.21⫾8.15 47.29⫾5.88

55 45

55.0 45.0 10.35⫾4.39 34.69⫾8.64** 50.60⫾6.90***

a

Puerto Rican: Puerto Rican/Puerto Rican American; other Latino: Hispanic/Latino/American/other. Determined by acculturation scale score; possible range 0 to 6, median⫽1. c Possible range 0 to 25, median⫽10. d SD⫽standard deviation. e BMI⫽body mass index; calculated as kg/m2. *Pⱕ0.05 statistical significance level (based on ␹2 or independent samples t test comparing diabetes and control groups). **Pⱕ0.01 statistical significance level (based on ␹2 or independent samples t test comparing diabetes and control groups). ***Pⱕ0.001 statistical significance levels (based on ␹2 or independent samples t test comparing diabetes and control groups). NOTE: Information from this table is available online at www.adajournal.org as part of a PowerPoint presentation. b

amid food groups (ranging from 44.2% for dairy to 93.8% for grains). The majority of the participants (93.5%) had heard about cholesterol, but only 39.8% had heard about saturated fat. Among those who had heard about fiber (78.1%), only 13.4% could recognize the definition of fiber. After adjusting for acculturation, age, food insecurity, diabetes status, and BMI, participants who did not have a partner (vs have partner; OR: 0.44; 95% CI: 0.23 to 0.86; P⫽0.016), or who were less educated (vs more educated; OR: 0.15; 95% CI: 0.07 to 0.30; P⬍0.001) were less likely to have a nutrition knowledge score above the median value. Employment status and Food Stamp Program participation were not significantly related to nutrition knowledge. Associations of Nutrition Knowledge with Psychosocial Factors and Food Label Use Most of the participants (82.1%) were familiar with food labels, and 79.1% of participants were confident in their ability to use them. However, Latinas with a higher nutrition knowledge level were more likely to be confident

about using food labels (88.0% vs 71.6%; P⫽0.004) and to be in the action stage of change for using food labels (66.3% vs 43.1%, respectively; P⫽0.001), for trying to eat less animal fat (79.3% vs 67.0%; P⫽0.050), less salt (84.8% vs 69.4%; P⫽0.011), and more fiber (66.3% vs 44.0%; P⫽0.002). Participants with a higher knowledge score were more likely to use food labels in selecting more healthful foods (38.0% vs 19.3%; P⫽0.003), foods that are low in fat (39.1% vs 24.8%; P⫽0.029), low in sugar (50.0% vs 33.0%; P⫽0.015), and low in sodium (39.1% vs 20.2%; P⫽0.003), and high in fiber (20.7% vs 10.1%; P⫽0.036). Women with a higher knowledge score were more likely to pay attention to label serving sizes (26.1% vs 12.8%; P⫽0.017), total carbohydrates (26.4% vs 11.0%; P⫽0.005), calories (45.7% vs 24.8%; P⫽0.002), ingredients (28.3% vs 16.5%; P⫽0.045), and nutrient claims (43.5% vs 23.9%; P⫽0.003). Multivariate analysis results showed that women with greater nutrition knowledge were more than twice as likely to use food labels to select healthful foods (OR: 2.60; 95% CI: 1.29 to 5.24; P⫽0.007) after controlling for education, age, and diabetes status; the covariates in this model, BMI, and

June 2008 ● Journal of the AMERICAN DIETETIC ASSOCIATION

963

Table 2. Determinants of consuming fruits and vegetables, meats, regular soft drinks, sweets, and salty snacks frequently (greater than median) among Latinas in Connecticuta Fruits and vegetables Less acculturatedb Highly acculturated High education Low education Nutrition knowledge scorec ⬎Median ⱕMedian Use FLd for healthful foods Nonuser Use FL for high-fiber foods Nonuser Use FL for low-sodium foods Nonuser Diabetes Control

Regular soft drinks

Meats

4™™™™™™™™™™™™™™™™™™™™™™™™™™ odds 0.98 (0.53-1.80) 1.03 (0.56-1.91) 1.00 1.00 2.27 (1.15-4.46)* 2.55 (1.28-5.09)** 1.00 1.00

Regular sweets/ desserts

Salty snacks

ratio (95% confidence interval) ™™™™™™™™™™™™™™™™™™™™™™™™™3 0.36 (0.19-0.66)*** 1.28 (0.69-2.39) 0.43 (0.23-0.81)** 1.00 1.00 1.00 1.03 (0.51-2.06) 1.56 (0.78-3.13) 0.94 (0.46-1.90) 1.00 1.00 1.00

1.30 (0.68-2.51) 1.00 0.88 (0.43-1.81) 1.00 2.87 (1.10-7.46)* 1.00

1.40 (0.72-2.75) 1.00 0.60 (0.30-1.19) 1.00

0.53 (0.27-1.05) 1.00 0.75 (0.38-1.49) 1.00

0.56 (0.28-1.09) 1.00 0.51 (0.26-1.01)* 1.00

0.62 (0.31-1.23) 1.00 0.71 (0.32-1.57) 1.00

—e

—

—

— 1.40 (0.75-2.64) 1.00

— 0.60 (0.32-1.12) 1.00

— 0.40 (0.21-0.75)** 1.00

— 0.36 (0.19-0.68)** 1.00

— 0.38 (0.17-0.85)* 1.00 1.36 (0.72-2.59) 1.00

a

Determined by multivariate logistic regression. All models are adjusted for age and body mass index. Determined by acculturation scale score; possible range⫽0 to 6, median⫽1. Possible range⫽0 to 25, median⫽10. d FL⫽food label. e Variable was not entered into model. *Pⱕ0.05. **Pⱕ0.01. ***Pⱕ0.001. NOTE: Information from this table is available online at www.adajournal.org as part of a PowerPoint presentation. b c

acculturation were not significantly related to food label use. Correlates of Food Intake Bivariate pooled analyses indicated that women with more nutrition knowledge were likely to consume meats, fruits, and vegetables more frequently and salty snacks less frequently (Table 3). Those with greater knowledge also tended to consume regular soft drinks (P⫽0.063) and sweets less frequently (P⫽0.065). Food intake patterns were different between food label users and nonusers (Table 3). Using food labels to select low-saturated fat, low-sodium, and high-fiber foods was related to a lower meat intake frequency. Food label users were more likely to consume fruits and vegetables and less likely to consume sweets, salty snacks, and regular soft drinks frequently (greater than median). They were also more likely to consume diet soft drinks frequently (57.1% of users vs 29.2% of nonusers, P⬍0.001). Looking at serving size, nutrient claims, or cholesterol information on food labels was not related to fruits, vegetables, meats, sweets, regular soft drinks, or salty snacks frequency of intake (data not shown). After adjusting for likely confounders, using food labels to select healthful foods was related to a 49% less likelihood of consuming sweets (Table 2) and a greater likelihood of consuming diet soft drinks (OR: 3.75; 95% CI: 1.78 to 7.92; P⫽0.001). In these models, using food labels to select highfiber foods was positively related to fruit and vegetable

964

June 2008 Volume 108 Number 6

intake, and using food labels to select low-sodium foods was negatively related to salty snack intake (P⬍0.05). The associations between nutrition knowledge and regular soft drinks, salty snacks, and sweets were attenuated when food label use was included in the models. Multivariate models indicated that education was positively related to consuming fruits and vegetables and meats. Being less acculturated was related to a decreased likelihood of consuming regular soft drinks and salty snacks. Intakes of legumes, grains, and dairy were not significantly related to nutrition knowledge, food label use, SES, or acculturation. DISCUSSION Overall nutrition knowledge, self-efficacy, and stages of change for using food labels were not significantly different between Latinas with and without diagnosed type 2 diabetes. The important role of RDs or diabetes educators in improving nutrition knowledge among Latinas with type 2 diabetes was supported by our results suggesting a greater level of nutrition knowledge if participants with this disease had seen an RD or a diabetes educator. The differences between Diabetes and Control groups in food label use and food intake were largely limited to looking at sugar information on food labels and preferring artificially sweetened foods rather than those made with regular sugar. A recent report (41) indicating a more healthful fat-related dietary intake score among Puerto Ricans with (vs without) diabetes did not examine the other food groups,

Table 3. Associations of nutrition knowledge and food label use with food group intake frequency (greater than median) among Latinas in Connecticut Fruits and Vegetables

Nutrition knowledge scoreb ⱕMedian ⬎Median Use food label to select . . . Healthful foods User Nonuserc Foods low in fat User Nonuser Foods low in saturated fat User Nonuser Foods low in sugar User Nonuser Foods high in fiber User Nonuser Foods low in sodium User Nonuser Use food label to look at . . . Carbohydrates User Nonuser Calories User Nonuser Ingredients User Nonuser

Regular Sweets/ Desserts

Regular Soft Drinks

Meats

Salty Snacks

n

%a

n

%a

n

%a

n

%a

n

%a

45 54

42.1 58.7*

45 53

42.5 57.6*

60 39

55.6 42.4

60 40

56.6 43.5

61 37

56.5 41.1*

31 68

55.4 47.6

24 74

43.6 51.7

24 75

42.9 52.1

21 79

37.5 55.6*

19 79

33.9 55.6**

34 65

54.0 47.8

26 72

42.6 52.6

30 69

47.6 50.4

23 77

36.5 57.0**

26 72

41.9 52.9

21 78

58.3 47.9

11 87

32.4 53.0*

18 81

50.0 49.4

15 85

41.7 52.5

13 85

36.1 52.5

43 56

52.4 47.9

37 61

46.3 51.7

33 66

40.2 55.9*

35 65

42.7 56.0

37 61

45.1 52.6

21 78

70.0 46.2*

10 88

33.3 52.4*

12 87

40.0 51.2

12 88

40.0 52.4

10 88

33.3 52.4*

31 68

53.4 48.2

21 77

36.8 54.6*

27 72

46.6 50.7

20 80

34.5 57.1**

18 80

31.0 57.1***

24 74

66.7 45.7*

16 82

44.4 50.9

13 86

36.1 52.8

11 89

30.6 55.3**

25 83

41.7 51.6

39 60

57.4 45.8

32 66

47.1 50.8

34 65

49.3 49.6

32 68

46.4 52.7

25 73

36.8 56.2**

31 68

70.5 43.9**

20 78

46.5 50.3

16 83

36.4 53.2*

19 81

43.2 52.6

17 81

38.6 52.6

a

Proportions of all participants within the knowledge and food label use categories who consumed food group at greater than median frequency. Possible range⫽0 to 25, median⫽10. c User: use food labels often; nonuser: use food labels sometimes or not at all. *Pⱕ0.05 statistical significance level (based on ␹2). **Pⱕ0.01 statistical significance level (based on ␹2). ***Pⱕ0.001 statistical significance level (based on ␹2). NOTE: Information from this table is available online at www.adajournal.org as part of a PowerPoint presentation. b

but Miller and colleagues (42) reported that sugar information was the most frequently used section of the food label among predominantly white women with type 2 diabetes. Considering the effectiveness of lifestyle and dietary modifications to prevent or delay type 2 diabetes (15) and the positive relationship between nutrition knowledge and weight control (43), the current findings suggest a need for nutrition education interventions in the study population. Moving beyond just preferring regular sugar vs artificial sweeteners, seems to be an obvious educational need. Saturated fat, fiber, and daily food group intake recommendations should be included in the educational interventions because these were among the topics that were least known by the participants.

In agreement with previous studies (16,17), Latinas with greater nutrition knowledge were considerably more likely to use food labels. They also had greater food label selfefficacy and were more likely to be at the action (vs preaction) stage of change for using food labels. Higher SES (education level) was a determinant of greater nutrition knowledge, but education was not related to food label use independent of nutrition knowledge. Previous studies support the positive associations between education and nutrition knowledge among Latinos (17,44). However, the studies that showed an association between a higher level of education and a greater understanding (45) or use of food labels (18,19) did not control for overall nutrition knowledge. Therefore, the current results are unique

June 2008 ● Journal of the AMERICAN DIETETIC ASSOCIATION

965

in suggesting that the association between level of education and food label use may be mediated by nutrition knowledge, highlighting the relevance of nutrition education for vulnerable communities. Another unique finding in this study was the attenuation of the associations between nutrition knowledge and food intake by food label use. It is plausible that nutrition knowledge may be influencing food intake indirectly through food label use. After adjusting for potential confounders, using food labels was related to about 62% less likelihood of consuming salty snacks, 49% less likelihood of consuming sweets, and about three times greater likelihood of consuming fruits and vegetables frequently. These findings are in agreement with previous reports showing positive associations of food label use with fruit and vegetable intake among African Americans (18), and with overall dietary quality (46) and fiber density (47) in ethnically mixed, nationally representative samples. The current findings suggest that culturally skilled food label education should be a strong priority to target food intake behaviors among Latinos. Independently of nutrition knowledge and food label use, the analyses also indicated a positive association between education and food intake (meats, fruits and vegetables). This finding can be a reflection of having a greater ability to afford more expensive foods in this sample with a relatively low SES, but it is also possible that a higher level of education might be related to greater awareness about the benefits of fruits and vegetables and changes in attitudes toward foods and their relationships with health. In agreement with other reports indicating more healthful diet profiles among less- (vs more) acculturated Mexican Americans (12,48), less-acculturated Latinas in this study were less likely to consume regular soft drinks and salty snacks even after controlling for likely confounders. These results point to the potential benefits of emphasizing the retention of healthful eating patterns within the Latino culture as this population acculturates. The limitations of this study include its observational design; hence, causality cannot be inferred from reported associations. Although this was a convenience sample, a variety of recruitment methods were used for capturing the diversity of the target community. Hispanics in the Northeastern United States are primarily from Puerto Rico; the population in Hartford, Connecticut is about 43% Hispanic and about 74% are Puerto Rican (49). Because the current study sample was 85% Puerto Rican, it fairly reflects the ethnic makeup at the study location, and generalizability of the results is limited to populations with similar characteristics. Because of the nature of the assessment, some questions could only be tested for face and content validity, and other study instruments showed adequate psychometric properties in this sample, but further testing for validity and reliability is needed for use in other Latino populations. CONCLUSIONS In view of the scarcity of data about the nutrition-related knowledge and behaviors of Latino subpopulations other than Mexican Americans, the current analyses were particularly important in helping close this gap for a mostly Puerto Rican sample.

966

June 2008 Volume 108 Number 6

●

●

●

●

There were very few differences in terms of nutrition knowledge, self-efficacy, and stage of change measures, food label use, and food intake between Diabetes and Control groups. Considering the high prevalence of diabetes among Latinos and the importance of healthful eating patterns for diabetes prevention and management, these results suggest a need for interventions to improve nutrition knowledge, skills, and food intake behaviors in this population. The study results indicated a higher nutrition knowledge level among Latinas with type 2 diabetes if they received services from RDs or diabetes educators. Because 36% of the women in the Diabetes group had never seen an RD or a diabetes educator, this points out the need for the provision of such services. The results suggested a multifaceted relationship between SES (education) and nutrition-related skills and behaviors: a direct association between SES and food intake independent of nutrition knowledge and skills, and also, a likely association through increased nutrition knowledge resulting in greater food label use, which in turn was related to a more healthful food intake pattern. Therefore, despite their socioeconomic limitations, populations with a low SES are likely to benefit from interventions aiming to increase instrumental nutrition knowledge that is based on MyPyramid (50) and food label education that strengthen the necessary skills needed to improve dietary intake. The positive associations of acculturation with soft drink and salty snack intakes point out the potential benefits of emphasizing the retention of healthful lifestyle behaviors within the Latino culture as individuals acculturate. This can be a valuable strategy for interventions targeting health disparities, especially against obesity and diabetes, in minority populations.

This work was funded by the United States Department of Agriculture Food Stamp Nutrition Education grant and the Connecticut Latino Health Disparities Center (Principal investigator Dr Pérez-Escamilla, National Institutes of Health-National Center on Minority Health and Health Disparities grant no. 1P20MD001765-01). The authors thank the study participants, Hispanic Health Council staff, and Lisa Phillips at the University of Connecticut for her administrative support. References 1. Centers for Disease Control and Prevention. Diabetes disabling, deadly, and on the rise. 2008. Centers for Disease Control and Prevention Web site. http://www.cdc.gov/diabetes/nccdphp/publications/ aag/ddt.htm. Accessed April 14, 2008. 2. Hu F, Manson J, Stampfer M, Colditz G, Liu S, Solomon C, Willett W. Diet, lifestyle and the risk of type 2 diabetes mellitus in women. N Engl J Med. 2001;345:790-797. 3. Montonen J, Jarvinen R, Kneckt P, Heliovaara M, Reunanen A. Consumption of sweetened beverages and intakes of fructose and glucose predict type 2 diabetes occurrence. J Nutr. 2007;137:14471454. 4. Schulze MB, Manson JE, Ludwig DS, Colditz GA, Stampfer MJ, Willett WC, Hu FB. Sugar-sweetened beverages, weight gain, and incidence of type 2 diabetes in young and middle-aged women. JAMA. 2004;292:927-934. 5. Van Dam RM, Rimm EB, Willett WC, Stampfer MJ, Hu FB. Dietary patterns and risk for type 2 diabetes mellitus in US men. Ann Intern Med. 2002;136:201-209. 6. Ledikwe JH, Blanck HM, Kettel Khan L, Serdula MK, Seymour JD,

7. 8. 9.

10.

11. 12.

13.

14.

15.

16. 17.

18.

19. 20.

21.

22. 23.

24.

25.

26. 27.

Tohill BC, Rolls BJ. Dietary energy density is associated with energy intake and weight status in US adults. Am J Clin Nutr. 2006;83: 1362-1368. Montonen J, Jarvinen R, Heliovaara M, Reunanen A, Aromaa A, Knekt P. Food consumption and the incidence of type II diabetes mellitus. Eur J Clin Nutr. 2005;59:441-448. Montonen J, Knekt P, Harkanen T, Jarvinen R, Heliovaara M, Aromaa A, Reunanen A. Dietary patterns and the incidence of type 2 diabetes. Am J Epidemiol. 2005;161:219-227. Kanjilal S, Gregg EW, Cheng YJ, Zhang P, Nelson DE, Mensah G, Beckles GL. Socioeconomic status and trends in disparities in 4 major risk factors for cardiovascular disease among US adults, 1971-2002. Arch Intern Med. 2006;166:2348-2355. Thompson FE, Midthune D, Subar AF, McNeel T, Berrigan D, Kipnis V. Dietary intake estimates in the National Health Interview Survey, 2000: Methodology, results, and interpretation. J Am Diet Assoc. 2005;105:352-363. Trudeau E, Kristal AR, Li S, Patterson RE. Demographic and psychosocial predictors of fruit and vegetable intakes differ: Implications for dietary interventions. J Am Diet Assoc. 1998;98:1412-1417. Dixon LB, Sundquist J, Winkleby M. Differences in energy, nutrient, and food intakes in a US sample of Mexican-American women and men: Findings from the Third National Health and Nutrition Examination Survey, 1988-1994. Am J Epidemiol. 2000;152:548-557. Mazur RE, Marquis GS, Jensen HH. Diet and food insufficiency among Hispanic youths: Acculturation and socioeconomic factors in the third National Health and Examination Survey. Am J Clin Nutr. 2003;78:1120-1127. Neuhouser M, Thompson B, Coronado G, Solomon C. Higher fat intake and lower fruit and vegetables intakes are associated with greater acculturation among Mexicans living in Washington State. J Am Diet Assoc. 2004;104:51-57. Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, Nathan DM. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346:393403. Petrovici DA, Ritson C. Factors influencing consumer dietary health preventative behaviors. BMC Public Health. 2006;6:222. Boulanger PM, Perez-Escamilla R, Himmelgreen D, Segura-Millan S, Haldeman L. Determinants of nutrition knowledge among low-income, Latino caretakers in Hartford, Conn. J Am Diet Assoc. 2002; 102:978-981. Satia JA, Galanko JA, Neuhouser ML. Food nutrition label use is associated with demographic, behavioral, and psychosocial factors and dietary intake among African Americans in North Carolina. J Am Diet Assoc. 2005;105:392-402. Neuhouser ML, Kristal AR, Patterson RE. Use of food nutrition labels is associated with lower fat intake. J Am Diet Assoc. 1999;99:45-53. Gans KM, Burkholder GJ, Risica PM, Lasater TM. Baseline fatrelated dietary behaviors of white, Hispanic, and black participants in a cholesterol screening and education project in New England. J Am Diet Assoc. 2003;103:699-706. Fitzgerald N. Identification of Risk Factors Mediating the Associations of Socioeconomic, Cultural, and Demographic Factors with Type 2 Diabetes Across Ethnic Groups [Doctoral dissertation]. University of Connecticut, Storrs, CT, 2004. DeLeon J. Household Composition, Acculturation, and Diet Among Low-Income Puerto Ricans in Hartford, Connecticut [Doctoral dissertation]. University of Florida, Tampa, FL, 2000. Perez-Escamilla R, Himmelgreen D, Segura-Millan S, Gonzalez A, Ferris AM, Damio G, Bermudez-Vega A. Prenatal and perinatal factors associated with breast-feeding initiation among inner-city Puerto Rican women. J Am Diet Assoc. 1998;98:657-663. Haldeman L. Development and Impact of a Food Label Education Intervention on Food Label Knowledge, Self-Efficacy and Stage of Change Among Latinos: A Randomized Community Trial [Doctoral dissertation]. University of Connecticut, Storrs, CT, 2001. Michigan Diabetes Research and Training Center. Diabetes care profile survey instrument. Michigan Diabetes Research and Training Center Web site. http://www.med.umich.edu/mdrtc/profs/survey.htm. Accessed April 14, 2008. Sheard CM. How effective is our advice to diabetics? A preliminary evaluation. Human Nutr Appl Nutr. 1984;38:138-141. Glanz K, Patterson RE, Kristal AR, DiClemente CC, Heimendinger J, Linnan L, McLerran DF. Stages of change in adopting healthy diets: Fat, fiber, and correlates of nutrient intake. Health Educ Q. 1994;21: 499-519.

28. Talbot F, Nouwen A, Gingras J, Gosselin M, Audet J. The assessment of diabetes-related cognitive and social factors: The multidimensional diabetes questionnaire. J Behav Med. 1997;20:291-312. 29. Perez-Escamilla R, Ferris AM, Drake L, Haldeman L, Peranick J, Campbell M, Peng YK, Burke G, Bernstein B. Food stamps are associated with food security and dietary intake of inner-city preschoolers from Hartford, Connecticut. J Nutr. 2000;130:2711-2717. 30. CDC National Center for Health Statistics. National Health and Nutrition Examination Survey, 1999-2000 Questionnaire Files. Centers for Disease Control and Prevention Web site. http://www.cdc.gov/ nchs/about/major/nhanes/quest99_00.htm. Accessed December 13, 2007. 31. Bowlin SJ, Morrill BD, Nafziger AN, Lewis C, Pearson TA. Reliability and changes in validity of self-reported cardiovascular disease risk factors using dual response: The Behavioral Risk Factor Survey. J Clin Epidemiol. 1996;49:511-517. 32. Song Y, Manson JE, Buring JE, Liu S. A prospective study of red meat consumption and type 2 diabetes in middle-aged and elderly women: The Women’s Health Study. Diabetes Care. 2004;27:2108-2115. 33. Herman WH, Smith PJ, Thompson TJ, Engelgau MM, Aubert RE. A new and simple questionnaire to identify people at increased risk for undiagnosed diabetes. Diabetes Care. 1995;18:382-387. 34. Frisancho A. New standards of weight and body composition by frame size and height for assessment of nutritional status of adults and the elderly. Am J Clin Nutr. 1984;40:808-819. 35. United States Department of Agriculture. The Food Guide Pyramid. US Department of Agriculture, Food and Nutrition Information Center Web site. http://www.nal.usda.gov/fnic/Fpyr/pmap.htm. Accessed June 20, 2007. 36. Prochaska JO, Redding CA, Evers KE. The Transtheoretical model and stages of change. In: Glanz K, Rimer BK, Lewis FM, eds. Health Behavior and Health Education. 3rd ed. San Francisco: John Wiley and Sons; 2002:99-120. 37. Baranowski T, Perry CL, Parcel GS. How individuals, environments, and health behavior interact: Social cognitive theory. In: Glanz K, Rimer BK, Lewis FM, eds. Health Behavior and Health Education. 3rd ed. San Francisco: John Wiley and Sons; 2002:165-184. 38. Bickel G, Nord M, Price C, Hamilton W, Cook J. Measuring Food Security in the United States. Guide to Measuring Household Food Security. Alexandria, VA: US Department of Agriculture; 2000. 39. Ball K, Crawford D. Socioeconomic status and weight change in adults: A review. Soc Sci Med. 2005;60:1987-2010. 40. Anderson A, Damio G, Himmelgreen D, Peng YK, Segura Perez S, Perez-Escamilla R. Social capital, acculturation, and breastfeeding initiation among Puerto Rican women in the United States. J Hum Lact. 2004;20:39-45. 41. Melnik TA, Spence MM, Hosler AS. Fat-related dietary behaviors of adult Puerto Ricans, with and without diabetes, in New York City. J Am Diet Assoc. 2006;106:1419-1425. 42. Miller CK, Probart CK, Achterberg CL. Knowledge and misconceptions about the food label among women with non-insulin-dependent diabetes mellitus. Diabetes Educ. 1997;23:425-432. 43. Klohe-Lehman DM, Freeland-Graves J, Anderson ER, McDowell T, Clarke KK, Hanss-Nuss H, Cai G, Puri D, Milani TJ. Nutrition knowledge is associated with greater weight loss in obese and overweight low-income mothers. J Am Diet Assoc. 2006;106:65-75. 44. Haldeman L, Perez-Escamilla R, Ferris AM, Drake L, Himmelgreen DA, Bonello H, Gonzalez A, Segura-Millan S, Mendez I, Bermudez A, Romero-Daza N. Development of a color-coded bilingual food label for low-literacy Latino caretakers. JNE. 2000;32:152-160. 45. Rothman RL, Housam R, Weiss H, Davis D, Gregory R, Gebretsadik T, Shintani A, Elasy TA. Patient understanding of food labels: The role of literacy and numeracy. Am J Prev Med. 2006;31:391-398. 46. Perez-Escamilla R, Haldeman L. Food label use modifies association of income with dietary quality. J Nutr. 2002;132:768-772. 47. Bhargava A. Socio-economic and behavioural factors are predictors of food use in the National Food Stamp Program Survey. Br J Nutr. 2004;92:497-506. 48. Smith WE, Day RS, Brown LB. Heritage retention and bean intake correlates to dietary fiber intakes in Hispanic mothers—Que Sabrosa Vida. J Am Diet Assoc. 2005;105:404-411. 49. US Census Bureau. 2005 American Community Survey. US Census Bureau Web site. http://factfinder.census.gov/home/saff/main.html?_ lang⫽en. Accessed August 6, 2007. 50. United States Department of Agriculture. US Department of Agriculture, MyPyramid Web site. www.mypyramid.gov. Accessed June 21, 2007.

June 2008 ● Journal of the AMERICAN DIETETIC ASSOCIATION

967