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published online 15 July 2013Asia Pac J Public HealthSaka, Maria L. Sayessian and Corilee A. Watters

Laila Rahman, Claudio R. Nigg, Lee S. Rosner, Cara S. Iversen, Hai V. Chung, Morris Lai, SusanFish Intake by Adolescents Is Related to Nutrient Intake but Not Lifestyle Factors

  

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Original Article

Fish Intake by Adolescents Is Related to Nutrient Intake but Not Lifestyle Factors

Laila Rahman, MS, MCom1, Claudio R. Nigg, PhD1, Lee S. Rosner, MS1, Cara S. Iversen, BS1, Hai V. Chung, BA2, Morris Lai, PhD1, Susan Saka, MEd1, Maria L. Sayessian, MA3 and Corilee A. Watters, PhD, RD1

AbstractNutrition during adolescence influences long-term health outcomes. Consumption of fish has many health benefits, yet few studies have investigated associations between fish intake and nutrient intake and lifestyle factors in adolescents. A cross-sectional study utilizing 24-hour dietary recalls obtained by in-person interviews investigated relationships between fish intake and demographic characteristics, nutrient intake, and lifestyle factors among adolescents (mean age = 15.5 years). Height, weight, and self-administered survey data were collected from 839 high school students who took part in the 2000-2004 Hawaii Nutrition Education Needs Assessment survey. About 8.5% of the students consumed fish, based on estimated EPA (eicosapentaenoic acid) + DHA (docosahexaenoic acid) intakes. Adolescents who consumed fish had higher intake of protein, water, B vitamins, magnesium, selenium, and zinc but consumed more calories, fat, saturated fat, and sodium. Considering the school health program’s potential to reach adolescents, more intensive school-based interventions can be directed to promote safe fish consumption and to encourage other positive lifestyle behaviors.

Keywordsadolescent, DHA, EPA, fish intake, lifestyle factor, nutrient intake

Introduction

A well-balanced diet, including limited intake of saturated fatty acids (FAs), cholesterol, and energy from soda and sweets and regular resistance, flexibility, and aerobic exercise are recom-mended for proper growth and prevention of obesity and subsequent chronic diseases in children and adolescents.1 Healthy diet and lifestyle may prevent outcomes such as hypertension, high

1University of Hawaii at Manoa, Honolulu, HI, USA2Harvard University, Honolulu, HI, USA3Hawaii Pacific University, Honolulu, HI, USA

Corresponding Author:Corilee A. Watters, Department of Human Nutrition, Food and Animal Sciences, College of Tropical Agriculture and Human Resources, University of Hawaii at Manoa, 1955 East West Road, Honolulu, HI 96822, USA. Email: cwatters@hawaii.edu

492560 APHXXX10.1177/1010539513492560Asia-Pacific Journal of Public HealthRahman et alresearch-article2013

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2 Asia-Pacific Journal of Public Health XX(X)

blood cholesterol, type 2 diabetes, heart disease, stroke, gallbladder disease, arthritis, sleep dis-turbances, anxiety, development of allergies, behavioral abnormalities, breathing problems, and certain types of cancers.1,2-5

Fish is a major nutritional resource with the potential to alleviate malnutrition throughout much of the Asia-Pacific region.6 Fish intake has been recommended based on numerous research findings of health benefits associated with the marine-based polyunsaturated long-chain omega-3 FAs, eicosapentaenoic acid (EPA; 20:5 n-3) and docosahexaenoic acid (DHA; 22:6 n-3).7 Fish, especially oily fish and fish oil supplements, are excellent sources of EPA and DHA.8 Health benefits of long-chain omega-3 FAs include reduced risk of coronary heart dis-ease (CHD), hypertension, several types of cancer, type 2 diabetes, clinical depression, stroke, rheumatoid arthritis, and poor visual acuity.9 Studies investigating long-chain omega-3 FAs were initiated after observations of low rates of myocardial infarction in communities that habit-ually eat fish.7,10 Clinical trials showed that consumption of EPA and DHA was related to reduced risk of CHD in a diseased population, but the evidence is not conclusive for the general population.11,12

The intake of long-chain omega-3 FAs is often inadequate in children and adolescents.3 In Western industrialized countries, people tend to have a diet rich in omega-6 FAs, which has been shown to be associated with poor health outcomes.4 The optimal ratio of omega-6 to omega-3 FAs should be about 4 to 1, but in actuality, it is closer to 17 to 1 and higher.4 This imbalance may create behavioral abnormalities as well as neurological and psychiatric disorders in both children and adolescents.3 Two servings of a variety of fish per week is recommended to achieve the health benefits of long-chain omega-3 FAs.1,5 Dietary recommendations for children and adoles-cents made by the American Heart Association and endorsed by the American Academy of Pediatrics are to eat more fish, especially oily fish, broiled or baked, in addition to other nutri-tious foods for the normal development of the brain and nervous system and primary prevention of cardiovascular disease, beginning at a young age.3,13

The evidence for a beneficial effect of fish consumption on CHD risk and overall health in the general population is not conclusive,3,11,12 leading some researchers to suggest that the benefits of long-chain omega-3 FA consumption may be confounded by other dietary and lifestyle factors.13 Using the Diabetic Control and Complications Trial database, Cundiff and colleagues14 found an inverse association between fish consumption and saturated FA intake. Osler and colleagues15 also found frequent fish consumption to be positively related to age, healthy diet score, and edu-cation and inversely associated with smoking and sedentary activity in middle-aged Danish adults. Higher fish intake was also found to be associated with healthy dietary patterns of chil-dren and adolescents in Spain.16 Thus, fish consumption might be related to healthier dietary and lifestyle patterns of adults and adolescents.

Adolescent health and lifestyle factors are a growing concern,17 yet there are few studies investigating associations between adolescent fish intake and other nutrient intakes and lifestyle factors. This study investigated whether adolescent fish intake was associated with differences in nutrient intake, whether intake of long-chain omega-3 FAs (EPA and DHA) among fish eaters was correlated with macronutrient and micronutrient intake, and whether lifestyle factors were associated with adolescent fish intake.

Adolescent fish consumption as indicated by long-chain omega-3 FA (EPA and DHA) intake, is hypothesized to be positively associated with healthy nutrient intake and negatively associated with saturated FA, cholesterol, alcohol, and energy intake from soda and sweets. Adolescent fish consumption is also hypothesized to be positively related to lifestyle factors such as exposure to discussions on nutrition, presence of adult role models, reduced consump-tion of meals away from home, eating school lunch, physical activity, and better perceived general health status.

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Methods

Participants/Procedures

The Hawaii State Department of Health carried out the 2000-2004 Hawaii Nutrition Education Needs Assessment (HNENA) survey, a cross-sectional study measuring the nutritional adequacy of high school students through the Curriculum Research & Development Group of the University of Hawaii at Manoa.18 Using PCSample, a computer software program developed by the Centers for Disease Control and Prevention (CDC), a total of 22 high schools in Hawaii were selected, in which all islands and charter schools were represented. In spring 2004, a total of 913 eligible students from 19 high schools voluntarily and anonymously participated in the study after obtain-ing written permission from parents or guardians. Two high schools chose not to participate in the study, and 1 school was excluded for obtaining a low number of parental permissions. The sam-pling details are described elsewhere.18

The data were collected by an in-person interviewer who administered a 24-hour dietary recall questionnaire and measured height and weight. A self-administered survey was used to collect data on nutrition-related lifestyle factors. Dietary recall data were analyzed using FoodWorks, nutritional analysis software utilizing data from the USDA Standard Reference 25, the Food and Nutrition Database for Dietary Studies, and the Canadian Nutrient File (version 6.0, The Nutrient Company, 2003, Long Valley, NJ). Data were reviewed to determine if any unreasonable outlying values were present; data for 1 participant with a daily intake of >14 000 kcal was excluded from the analysis. A total of 839 high school students’ responses were analyzed. Ethics exemption status was granted by the University of Hawaii Institutional Review Board.

Measures

Fish Consumption. EPA and DHA intake derived from analysis of 24-hour dietary recall data were used to determine whether or not fish was consumed. It was impossible to precisely distinguish those that ate fish from those that did not based solely on long-chain omega-3 FA intake because certain foods other than fish contain significant amounts of EPA and DHA. According to the USDA Nutrient Database19 2 large eggs (100 g) contain 58 mg of DHA, a significant, commonly consumed nonseafood source of long-chain omega-3 FAs. For the purposes of this study, we defined fish eaters based on consumption of greater than 58 mg of EPA + DHA, thereby avoiding the inclusion of students who ate 2 eggs but no fish in the fish-eating category. Using this cutoff, it is possible that students who consumed small amounts of fish low in omega-3 FAs were catego-rized as non–fish eaters. Even consuming 2 ounces of a fish low in EPA and DHA, such as farmed tilapia (135 mg EPA + DHA per 100 g), would provide more than 58 mg of EPA + DHA.19

Demographics, Weight, and Dietary Measures. Age in years, sex, ethnicity, and school grade were included in the demographic measures. Weight status categories were calculated using the body mass index (BMI). The CDC’s BMI-for-age growth charts (2000), which are different for girls and boys, were used to categorize underweight, healthy, overweight, and obese adolescents.20

Dietary measures encompassed both macronutrient and micronutrient intake, including min-erals and vitamins. The percentage of dietary kilocalories (%kcal) contributed by each macronu-trient was calculated using 4 kcal/g for protein and carbohydrate, 9 kcal/g for fat, and 7 kcal/g for alcohol.

Lifestyle and General Health Measures. Lifestyle measures assessed by the student survey included whether or not there was discussion about good nutrition with family during the past month (“yes” was coded as 1; “no” was coded as 0), whether or not adults set a good example by

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selecting healthy foods (4 items were dichotomized: “agree” and “strongly agree” were coded as 1; “disagree” and “strongly disagree” were coded as 0), weekly frequency of eating out (5 items were dichotomized: “hardly ever” and “1-3 times per week” were coded as 1; “4-6 times a week,” “7-10 times a week,” and “11 or more times a week” were coded as 0), weekly frequency of eat-ing school lunch (5 items were dichotomized: “never,” “hardly ever,” and “1-2 times per week” were coded as 1; “every day” and “3-4 times a week” were coded as 0), frequency of moderate physical activity per week (8 numeric responses of 0-7 were dichotomized: “5-7” were coded as 1; “0-4” were coded as 0), frequency of strenuous physical activity per week (8 numeric responses of 0-7 were dichotomized: “3-7” were coded as 1; “0-2” were coded as 0), and self-perceived general health (5 responses were dichotomized: “excellent” and “very good” were coded as 1; “good,” “fair,” and “poor” were coded as 0).

Analysis

Within no-fish-intake and fish-intake groups, the percentage of individuals falling into each demo-graphic category was calculated. A 1-sample Kolmogorov-Smirnov test was used to examine the distribution of the dietary variables. The χ2 and Mann-Whitney U tests were used to analyze dif-ferences in demographic characteristics and nutrient intake distribution, respectively, between groups who reported and did not report fish intake in the past 24 hours. The effect of demographic variables such as sex, age, BMI category, and ethnicity on the probability of individuals consum-ing fish were determined using multivariate logistic regression analysis. Multiple linear regres-sion, including all nutrients in the model, was performed to determine the effect of fish consumption on intake of individual nutrients. The analysis was run with and without the variable “sex” in the model to test for Sex × Nutrient interactions. When included in the model, this variable was not significant (P = .14) and did not change the significance of any nutrient variable, so it was not included in the final model. Spearman’s ρ was calculated to analyze correlations between long-chain omega-3 FA (EPA plus DHA) intake and other macronutrient and micronutrient intake. Logistic regression was used to compare the odds of having a particular lifestyle factor between no-fish-intake and fish-intake groups after controlling for sex, age, BMI, and ethnicity.

Results

Demographic Characteristics

The mean age of the students was 15.5 years; 56.6% were female, and about three-quarters were Asian and Pacific Islanders (API; Table 1). About 8.5% (n = 71) of the high school students sur-veyed were categorized as having eaten fish in the 24 hours prior to the survey (Table 1). A greater proportion of students aged 16+ consumed fish compared with 14- to 15-year-old stu-dents, but there was no association between sex, grade in school, or ethnicity and fish intake (Table 1). Although not statistically significant, 9.2% of Asian and Pacific Islander adolescents consumed fish compared with 5.0% of Caucasian adolescents.

More than 35% of the students surveyed were overweight or obese, but BMI category was not significantly associated with fish intake (Table 1). Among all the adolescents, younger adoles-cents were more likely to be overweight (P < .05) and obese; male adolescents were significantly more obese than female adolescents (P < .001; not shown).

Energy and Nutrient Intake

Adolescents who consumed fish had higher intake of numerous nutrients, with dietary fiber being the only substance with significantly reduced intake among students who ate fish (Table 2). Fish

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intake was associated with increased protein and fat intake but not carbohydrate or sugar intake. Those who consumed fish benefited from increased intake of water, vitamin B6, vitamin B12, folate, niacin, riboflavin, thiamin, pantothenic acid, magnesium, selenium, and zinc (Table 2). On the other hand, students who ate fish also consumed more calories, fat, saturated fat, phosphorus, and sodium. Results of multiple linear regression showed that fish intake increased cholesterol, total omega-3 FA, vitamin B6, pantothenic acid, and selenium intake and reduced percentage kilocalories of saturated FA, total omega-6 FA, folate, thiamin, and zinc intake.

In terms of macronutrient distribution, percentage kilocalories of protein and fat were higher in fish eaters and percentage kilocalories of carbohydrates were higher among those not eating fish. Correlations between long-chain omega-3 FAs and other macronutrient and micronutrient intake are shown in Table 3. Long-chain omega-3 FA intake was positively correlated with per-centage kilocalories of protein and selenium intake but negatively correlated with percentage kilocalories intake of saturated FAs and cholesterol.

Fish Consumption and Lifestyle Factors

Logistic regression analysis showed no significant associations between fish intake and any of the lifestyle factors when demographic variables and BMI were accounted for (Table 4). There were several positive lifestyle factors that were present in a majority of the respondents indepen-dent of fish intake: adults setting a good example by selecting healthy foods, eating out fewer than 3 times per week, eating school lunch at least sometime each week, and engaging in strenu-ous physical activity at least 3 days each week (Table 4). Factors that decreased the odds of

Table 1. Demographic Characteristics of Adolescents According to Reported Fish Intake.

Characteristic

Fish Intake (Total n = 71),

% (n)

No Fish Intake (Total n = 768),

% (n)Crude Odds Ratio

(95% CI)Adjusted Odds Ratioa

(95% CI)

Sex Male (n = 364) 46.5 (33) 43.1 (331) 1.00 (1.00, 1.00) 1.00 (1.00, 1.00) Female (n = 475) 53.5 (38) 56.9 (437) 0.87 (0.54, 1.42) 0.88 (0.54, 1.45)Age in years 14-15 (n = 431) 38.0 (27) 52.6 (404) 0.55 (0.35, 0.91)b 0.53 (0.32, 0.88)b

16 + (n = 408) 62.0 (44) 47.4 (364) 1.00 (1.00, 1.00) 1.00 (1.00, 1.00)(Mean age ± SD) (15.8 ± 0.8) (15.5 ± 0.9) BMI category Underweight (n = 18) 2.8 (2) 2.1 (16) 1.42 (0.32, 6.40) 1.27 (0.28, 5.79) Healthy (n = 520) 59.2 (42) 62.2 (478) 1.00 (1.00, 1.00) 1.00 (1.00, 1.00) Overweight (n = 148) 19.7 (14) 17.5 (134) 1.19 (0.63, 2.24) 1.36 (0.71, 2.58) Obese (n = 153) 18.3 (13) 18.2 (140) 1.06 (0.55, 2.02) 1.09 (0.56, 2.11)(Mean BMI ± SD) (24.4 ± 6.7) (23.8 ± 5.4) Ethnicity Caucasians (n = 140) 9.9 (7) 17.3 (133) 1.00 (1.00, 1.00) 1.00 (1.00, 1.00) API (n = 618) 80.3 (57) 73.0 (561) 1.93 (0.86, 4.33) 2.03 (0.90, 4.57) Others (n = 81) 9.9 (7) 9.6 (74) 1.78 (0.60, 5.32) 1.76 (0.59, 5.24)Grade in school 9 (n = 157) 9.9 (7) 19.5 (150) 0.41 (0.12, 1.36) —c

10 (n = 587) 78.9 (56) 69.1 (531) 0.93 (0.35, 2.44) —c

11 (n = 46) 4.2 (3) 5.6 (43) 0.61 (0.14, 2.73) —c

12 (n = 49) 7.0 (5) 5.7 (44) 1.00 (1.00, 1.00) —c

Abbreviations: CI, confidence interval; SD, standard deviation; API, Asian and Pacific Islanders.aOdds ratio for effect of demographic variables and BMI on probability of fish intake using multivariate regression model adjusting for sex, age category, ethnicity, and BMI category.bP < .05.cGrade was not included in the multivariate regression model because it was highly confounded with age.

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6 Asia-Pacific Journal of Public Health XX(X)

reporting good to excellent health status were female sex and increasing BMI. Increasing BMI and API ethnicity decreased the odds that students talked with their family about healthy nutri-tion. Increasing BMI increased the odds of rarely or never eating out, as did decreasing age. Increasing age and API ethnicity increased the odds of eating a school lunch. Female participants had greatly reduced odds of performing either moderate exercise 5 or more times or strenuous exercise 3 or more times each week. API ethnicity also strongly reduced the odds of performing strenuous activity more than 3 times each week.

Female participants and Asian and Pacific Islanders engaged in less physical activity than males or other ethnic groups (data not shown). On the other hand, less than 30% of the students surveyed felt that they had very good to excellent general health; male participants and Caucasians

Table 2. The Median Nutrient Intake of Adolescents Who Consumed Fish (n = 71) and Did Not Consume Fish (n = 768) Compared Using the Mann-Whitney U Test and Multiple Linear Regression Analysis for Probability of Eating Fish for Model Including All Listed Nutrients.

Mann-Whitney Test Multiple Linear Regression

NutrientFish Intake, Median

(Interquartile Range)No Fish Intake, Median (Interquartile Range) Z Statistic

Regression Parameter Estimate

Adjusted P Value

Kilocalories (kcal) 2526 (1533-3065) 1891 (1357-2539) 0.0003 −0.0002 .84Soda (kcal) 55.1 (48.6-61.9) 0 (0-159) 0.08 0.0001 .17Sweets (kcal) 5.40 (3.60-7.27) 0 (0-52.2) 0.39 0.0002 .02

Protein (g) 106 (77.3-141) 69.4 (46.9-97.4) <0.0001 0.0004 .92Protein (%kcal) 17.7 (15.4-20.4) 14.6 (11.8-17.6) <0.0001 0.0185 .35Carbohydrate (g) 314 (182-394) 257 (186-358) 0.18 0.0003 .92Carbohydrate (%kcal) 48.5 (44.2-52.5) <0.0001 0.0101 .56Fiber (g) per 1000 kcal 4.15 (3.06-5.56) 0.0002 −0.0039 .31Total sugar (g) 85.8 (34.8-179) 86.3 (49.4-134) 0.71 0.0002 .65Sugar (%kcal) 15.5 (8.93-21.9) 18.4 (11.9-26.1) 0.015 −0.0010 .62Total FAs (g) 93.9 (58.1-118) 62.5 (41.9-90.1) <0.0001 0.0002 .98FA (%kcal) 34.2 (29.9-37.3) 30.4 (24.7-35.9) 0.0005 0.0137 .43Saturated FAs (g) 27.5 (17.8-36.3) 21.2 (13.5-30.6) 0.0006 0.0055 .12Saturated FA (%kcal) 10.3 (8.83-12.2) 10.0 (8.01-12.3) 0.38 −0.0162 .04Cholesterol (mg) 429 (234-744) 181.2 (113-306) <0.0001 0.0004 <.0001Alcohol (g) 0 (0-0) 0 (0-0) 0.59 −0.0008 .97Total n-3 FAs 0.86 (0.50-1.24) 0.30 (0.11-0.61) <0.0001 0.0578 <.0001Total n-6 FAs (g) 0.10 (0-0.25) 0 (0-0.21) 0.15 −0.0123 <.001Water (g) 2430 (1516-3209) 1875 (1331-2578) 0.004 0.00001 .19Caffeine (mg) 2.50 (0-44.2) 5.000 (0-31.7) 0.97 −0.0001 .72Vitamin A (RE) 631 (369-1002) 520 (274-978) 0.17 −0.00003 .81Vitamin B6 (mg) 1.64 (1.16-2.73) 1.34 (0.84-2.05) 0.002 0.0179 .02Vitamin B12 (µg) 4.37 (2.83-8.31) 3.36 (1.94-5.84) 0.001 0.0035 .23Vitamin C (mg) 63.0 (24.1-166) 64.9 (26.3-135) 0.95 0.0001 .62Vitamin D (µg) 2.89 (0.93-5.54) 2.50 (0.04-5.01) 0.07 −0.0075 .09Folate (µg) 332 (228-469) 279 (174-442) 0.049 −0.0002 .03Niacin (mg) 27.0 (18.5-37.3) 19.9 (13.2-28.3) <0.0001 0.0008 .65Riboflavin (mg) 2.07 (1.31-2.84) 1.55 (0.98-2.34) 0.0003 0.0276 .26Thiamin (mg) 1.70 (1.26-2.70) 1.40 (0.97-2.11) 0.008 −0.0480 .03Pantothenic acid (mg) 4.66 (3.37-6.05) 2.63 (1.54-3.95) <0.0001 0.0161 .01Calcium (mg) 673 (405-1021) 644 (362-964) 0.40 −0.00004 .38Iron (mg) 16.2 (11.6-20.8) 13.0 (8.69-18.6) 0.003 0.0033 .14Magnesium (mg) 267 (181.5-343) 194 (137-288) 0.0008 −0.0001 .58Phosphorus (mg) 1403 (950-1801) 1017 (699-1427) <0.0001 −0.00003 .52Selenium (µg) 94.5 (69.1-119) 45.1 (24.9-73.7) <0.0001 0.0014 <.0001Sodium (mg) 3351 (2259-4533) 2794 (1774-4180) 0.04 −0.0001 .20Zinc (mg) 10.3 (6.99-16.8) 9.12 (5.96-13.6) 0.008 −0.0087 <.001

Abbreviations: FA, fatty acid; RE, retinol equivalent.

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were more likely to claim very good to excellent health than female participants and other ethnic groups, respectively (data not shown). Less than half of the total sample had talked about good nutrition with their family the previous month, and again, the number was higher in Caucasians (data not shown).

Discussion

Adolescents who consumed fish were hypothesized to have higher intake of healthy nutrients and lower intake of saturated FA, cholesterol, and energy from soda and sweets than their non-fish-eating counterparts. Adolescents who consumed fish did in fact consume more of numer-ous essential nutrients, including protein and water, most water-soluble vitamins, and minerals magnesium, selenium, and zinc. However, fish intake was also associated with increased intake of calories, fat, saturated fat, and sodium, nutrients that are unhealthy when intake is excessive. Fish intake did not seem to influence intake of total sugar or sugar from soda and sweets. From these data, there is little evidence to suggest that adolescents who ate fish had improved diets overall.

In a study investigating the influence of consumers’ knowledge and health-related beliefs on fish consumption frequency in 5 European countries, Pieniak et al21 observed that factors such as interest in healthy eating, fish-related nutrition knowledge, and a strong belief that eating fish is healthy only had weak positive influences on fish consumption frequency. Increasing age and level of education were also shown to positively influence fish consumption frequency. In the present study, we have no data on objective nutrition knowledge or attitudes toward healthy eat-ing among the participants. Their young age and low level of education suggests that nutrition knowledge may be low and interest in healthy eating may be weak. Hedonistic, cultural, and

Table 3. Correlations Between Long-Chain Omega-3 FAs (EPA and DHA) From Fish and Other Nutrient Intake for Adolescents Who Reported Fish Intake (n = 71).a

Nutrient Spearman’s ρ Nutrient Spearman’s ρ

Kilocalories (kcal) 0.01 Water 0.14Kilocalories from soda 0.02 Caffeine 0.11Kilocalories from sweets 0.17 Vitamin A −0.09Kilocalories from soda and sweets 0.06 Vitamin B6 0.19Protein 0.14 Vitamin B12 0.07Protein (%kcal) 0.28* Vitamin C −0.04Carbohydrate 0.00 Vitamin D 0.18Carbohydrate (%kcal) 0.12 Folate −0.10Dietary fiber 0.08 Niacin 0.10Total sugar 0.07 Riboflavin 0.05Sugar (%kcal) 0.08 Thiamin 0.04Total FAs −0.07 Pantothenic acid 0.11Total FAs (%kcal) −0.21 Calcium −0.02Saturated FAs −0.10 Iron 0.00Saturated FAs (%kcal) −0.25* Magnesium 0.20Cholesterol −0.24* Phosphorus 0.11Alcohol 0.09 Selenium 0.32**Total omega-6 FAs 0.08 Sodium 0.08Total omega-6 FAs (%kcal) 0.07 Zinc 0.02

Abbreviations: FA, fatty acid; EPA, eicosapentaenoic acid; DHA, docosahexaenoic acid.a*P < .05; **P < .01.

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8

Tab

le 4

. A

dole

scen

t Li

fest

yle

Fact

ors

Acr

oss

Fish

-Inta

ke a

nd N

o-Fi

sh-In

take

Gro

ups

and

Logi

stic

Reg

ress

ion

Odd

s R

atio

s Pr

edic

ting

Fish

Inta

ke b

y A

dole

scen

ts.a

Fish

Inta

ke C

ateg

ory

Logi

stic

Reg

ress

ion

Res

ults

Logi

stic

Reg

ress

ion

Para

met

er E

stim

ates

for

Cov

aria

tes

Life

styl

e Fa

ctor

Fish

Inta

ke (

n =

71)

, Pe

rcen

tage

(n)

No

Fish

Inta

ke

(n =

768

), Pe

rcen

tage

(n)

Cru

de O

dds

Rat

io

(95%

CI)

Adj

uste

d O

dds

Rat

iob

(95%

CI)

Sex

(Fem

ale)

Age

BMI

Ethn

icity

(A

PI)

Ethn

icity

(O

ther

)

Ver

y go

od t

o ex

celle

nt g

ener

al h

ealth

st

atus

22.5

(16

)30

.7 (

236)

0.66

(0.

37, 1

.17)

0.66

(0.

36, 1

.19)

−0.

39**

*0.

08−

0.08

***

−0.

23−

0.13

In t

he p

ast

mon

th t

alke

d ab

out

good

nu

triti

on w

ith fa

mily

45.1

(32

)42

.5 (

326)

1.06

2 (0

.65,

1.7

3)1.

12 (

0.68

, 1.8

3)0.

098

−0.

020.

04**

−0.

40**

*0.

03

Adu

lts s

et g

ood

exam

ple

by s

elec

ting

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thy

food

s69

.0 (

49)

71.2

(54

7)1.

27 (

0.67

, 1.9

1)1.

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Rahman et al 9

social influences may be much more important factors determining fish consumption in this population. Willet and Stampfer22 observed that, in general, a positive correlation exists between intake of most nutrients and total energy intake. Increased fat and cholesterol intake among ado-lescents who consumed fish may simply reflect the fact that these students consumed more calo-ries; it may also be related to a greater propensity of fish eaters to consume other forms of animal protein.

Long-chain omega-3 FA intake was hypothesized to be positively correlated with healthy nutrient intake and negatively associated with saturated FA and energy intake from soda and sweets. Several studies have found an inverse association between fish consumption and satu-rated FA intake along with healthier diet intake in diseased populations.14,15 The present study found a negative correlation between long-chain omega-3 FA intake and saturated fat and cho-lesterol intake among fish eaters, despite the finding that fish intake was associated with increased fat and saturated fat intake. Although the quantity of fish eaten would have a direct effect on long-chain omega-3 FA intake, the species of fish could have a greater effect, given that concentrations of EPA and DHA can vary by more than an order of magnitude between spe-cies. The highest levels of long-chain omega-3 FAs are found in pelagic and salmon species, whereas the lowest are found in freshwater and desmersal marine species.23 Species with low levels of long-chain omega-3s are typically among those used by fast food restaurants or avail-able breaded and frozen in the supermarket.24 Among fish eaters, the quantity of long-chain omega-3s in the diet may be associated with differences in where food is obtained, how it is prepared, and lifestyle factors not included in this survey. Fish can be part of a healthy diet or part of an unhealthy diet.

Consumption of fish by adolescents was not associated with any positive lifestyle factors, although the trend was for fish eaters to eat out less often and eat school lunch more often. Although sex, BMI, and ethnicity had little influence on fish consumption in this group of stu-dents, these variables did affect the lifestyle factors assessed in this study. In particular, Asian and Pacific Islanders, with a trend toward a higher percentage of fish eaters, also were more likely to consume school lunches. Asian and Pacific Islanders also displayed reduced levels of physical activity and were less likely to discuss good nutrition with their families. These results are consistent with those of Moy et al,25 who found increased prevalence of obesity and con-sumption of unhealthy foods and beverages among Native Hawaiian and Pacific Islanders in California.

Although fish intake did not differ significantly between female and male students, female students were significantly less likely to engage in moderate or strenuous exercise and less likely to report good to excellent health status. Parents play an important role in helping children adopt and practice healthy lifestyles, but there was no association between fish consumption and adults setting a good example by selecting healthy foods or more frequent discussions about good nutri-tion with the family. Most parents consider discussions about good nutrition with children impor-tant,16 but only two-fifths of the high school students reported that parents actually discussed healthy diets.

Limiting eating out and regular physical activity are other important recommendations to prevent adolescent obesity.17,26 However, we did not find eating out and physical activity to have any relation with fish intake. With regard to eating school lunch, 73% of the adolescents ate school lunch frequently, whereas 69% of the students agreed that school lunch was healthy, and 60% indicated that they liked the foods. School lunches, however, are not the only component of the food environment at school. It has been shown that schools that offer more free and reduced-price lunches are less likely to offer a diverse healthy food menu outside lunch and more likely to allow the purchase of unhealthy snacks than schools with low enrollment.15 There are improve-ment opportunities within school lunch programs as well as elsewhere in the food environment that can influence adolescents’ healthy lifestyles and rates of fish consumption.

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10 Asia-Pacific Journal of Public Health XX(X)

Limitations

A single 24-hour dietary recall may have limited ability to predict usual intake for episodically con-sumed foods,27 such as fish. A food frequency questionnaire related to fish consumption would have been helpful, but this study was not originally intended to measure association of adolescent fish consumption with nutrition and lifestyle factors. However, increasing the number of participants rather than increasing the number of days of diet recorded per participant may be most effective in reducing variability when comparing nutrient intake between groups.28 The very large sample size in the present study may have mitigated some of the within-participant variability in nutrient intake not captured by the single recall. Self-reported information on lifestyle factors may have been affected by social desirability and social approval bias; response bias also tends to vary depending on the sensitivity of the issues.29 However, 24-hour dietary recalls have been shown to have less error than food diaries, whereas food frequency questionnaires tend to overestimate dietary intake.30 To ensure validity of the data, standard practices were observed in data collection, such as the use of codes instead of names in collecting and linking data.18 The study did not include parents’ sociodemo-graphic variables, which could better explain the variability in nutrition and lifestyle factors. Other parental factors such as BMI and lifestyle factors such as eating and physical activity were not mea-sured. These factors could strongly influence the observed associations. The quantitative nature of the study also does not allow us to understand adolescents’ context and motivation for eating and not eating fish and pursuing healthy or unhealthy behaviors. Future studies should be carried out with an intervention design that will allow inference to causation. Situation analysis using qualitative meth-ods can be used with participation of the adolescents, parents, and teachers to inform the intervention activities in order to effectively address the barriers that limit healthy diet and lifestyles.

Conclusion

Fish intake is associated with some healthy and also some unhealthy nutrient intake indicators but not associated with positive lifestyle factors. It may be that different fish or fish preparations are specifically related to healthy or unhealthy behaviors (and outcomes). As part of growing health promotion activities in the fast food industry, inclusion in the menu of vegetable and fish items that are not optimally prepared may be causing a positive association between fish intake and unhealthy nutrients. A majority of adolescents eat school lunch and spend a considerable amount of time in schools. Therefore, school health programs have the potential to educate adolescents about healthy diets, offer healthful foods, create conducive environments, and promote positive lifestyles.15 In fact, the lack of association between fish intake and positive lifestyle factors indicates that promotion of lifestyle factors, including physical activity, has to be separately reinforced; eating a healthy diet does not automatically translate into a positive lifestyle, whereas both are important to build a healthy society.

Acknowledgment

We would like to acknowledge Sandra Shimabukuro, Lorna Afaga, Vicki Bunao, and Heather Trundle who coauthored the Hawaii Nutrition Education Needs Assessment (HNENA) Final Report 2000-2004.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publi-cation of this article: The study uses data from Hawaii Nutrition Education Needs Assessment (HNENA),

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Rahman et al 11

which was funded by the Hawaii State Department of Health. The East-West Center sponsored LR’s time to this study.

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