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Perceived Diffusion Attributes of Nutrition Education Curricula
TUESDAY, OCTOBER 20
RESEARCH & PRACTICE INNOVATIONS: TRANSLATING RESEARCH INTO DIETETICS PRACTICE (PART 2) Perceived Diffusion Attributes of Nutrition Education Curricula Author(s): A. Diker,1 L. M. Walters,2 L. Cunningham-Sabo1; 1Food Science and Human Nutrition, Colorado State University, Fort Collins, CO, 2Cooking with Kids, Inc., Santa Fe, NM Learning Outcome: The participant will be able to identify perceived attributes that affect the rate of adoption of nutrition education curricula. Background: Understanding perceived attributes of successful nutrition education curricula is useful in identifying appropriate curriculum dissemination strategies. Rogers’ Diffusion of Innovations Theory identifies five perceived attributes (compatibility, relative advantage, complexity, trialability, and observability) which affect diffusion and adoption rates. Results of a web-based survey were analyzed to identify perceived attributes contributing to nutrition education curricula adoption. Methods: Secondary data analysis was performed of survey results from 109 respondents who registered to download free fruit and vegetable nutrition education lessons. Construct validity of questionnaire items was assessed using principal axis factor analysis. Cronbach’s alpha was used to determine internal consistency reliability of questions based on factor analysis results. Predictors of intended future use of tasting lessons were assessed via stepwise multiple regression. Results: Factor analysis revealed the five perceived attributes accounted for 31.9% of cumulative variance. Sixteen questions with factor loading greater than 0.40 were retained from the initial set of 42 questions. Reliability testing based on factor loading resulted in adequate Cronbach’s alpha values for trialability (.611), compatibility (.613), complexity (.613), and relative advantage (.685). Stepwise multiple regression revealed trialability significantly predicted future planned usage of tasting lessons (p⫽0.006).
Using the USDA Multivitamin/Mineral Calculator to Apply Research from the Dietary Supplement Ingredient Database (DSID) to Assess Nutrient Intake Author(s): J. M. Roseland,1 K. W. Andrews,1 J. M. Holden,1 C. Zhao,1 A. Middleton,1 M. Feinberg,1 L. W. Douglass,2 J. T. Dwyer,3 M. F. Picciano,3 L. G. Saldanha,3 C. T. Sempos,3 R. Bailey3; 1Nutrient Data Laboratory, USDA/ ARS/BHNRC, Beltsville, MD, 2Consulting Statistician, Longmont, CO, 3Office of Dietary Supplements, NIH, Bethesda, MD Learning Outcome: Participants will be able to discuss how the MVM calculator applies research results to provide estimated vitamin and mineral levels in adult MVMs. Background: Complete information on nutrient intake from dietary supplements, as well as from foods, is necessary to evaluate diet-health relationships. To address this need, the Dietary Supplement Ingredient Database-Release One (DSID-1) was recently developed in collaboration between USDA and the NIH Office of Dietary Supplements. DSID-1 includes analytically-based regression predictions for ingredient levels in adult MVMs (defined as ⬎3 vitamins) and a multivitamin/mineral (MVM) calculator. The interactive calculator provides estimates and variability measures for 18 nutrients in adult MVMs when users enter labeled values. Methods: Nutrient estimates are based upon data from a nationwide study of representative adult MVMs conducted by USDA from 2006 to 2008. About 115 products were purchased in six locations from various market channels. Results of nutrient analyses at qualified laboratories compared mean percent difference between analyzed and labeled levels using regression analysis. Predicted values and standard errors of the mean for 18 nutrients were calculated. Results: Predicted mean difference from label at the most common level per nutrient ranged from 0 to 10% above label for 11 nutrients, 10 to 20% for 4 nutrients, and ⬎20% above label for 3 nutrients.
Conclusion: Trialability, the degree to which an innovation may be experimented with prior to adoption, likely is an important factor for adoption and implementation of nutrition education curricula. Programs should consider incorporating a trialability component, such as a free introductory lesson or curriculum pilot-testing by a small number of educators, to enhance curriculum adoption.
Conclusion: The calculator features user-friendly applications. Researchers and other professionals can employ the calculator or DSID-1 data files to acquire analytically-derived nutrient estimates for generic forms of adult MVMs, create electronic files, and combine supplement information with food data for total nutrient estimates. DSID-1 aids appraisal of nutrient intakes from adult MVMs, complementing the USDA National Nutrient Database for Standard Reference for foods.
Funding Disclosure: USDA CSREES NRI 2007-05062
Funding Disclosure: Office of Dietary Supplements/NIH
Body Image Self-Perception Author(s): C. L. Atwell,1 D. Rigassio Radler,2 J. Ziegler,2 R. Touger-Decker,3 H. Khan4; 1Norton Community Hospital, Norton, VA, 2Nutritional Sciences, University of Medicine and Dentistry of New Jersey, Newark, NJ, 3Nurtitional Sciences, University of Medicine and Dentistry, Newark, NJ, 4University of Medicine and Dentistry of New Jersey, Newark, NJ Learning Outcome: Dietitians and other health professionals, by knowing that some people desire what clinically is not a healthy weight, will be able to customize education to those individual’s specific needs. Objective: To determine relationships between self-reported and desired body weight, calculated current and desired body mass index (BMI), and perceived current and desired body image among University of Virginia at Wise (UVAWise) Freshmen students. Methods: A descriptive survey design was used with a cross-sectional convenience sample of students ⱖ18 years old (n⫽139). The self-administered anonymous, questionnaire included demographic data, self-reported height, weight, desired weight, and perceived current and desired body image. Descriptive statistics, Pearson correlation coefficients, and Chi-Square test were used. Results: Of the 139 questionnaires, 86% (n⫽120) were usable; 52.5% were male; mean age⫽18.43 (range⫽18-25 years). There were strong statistically significant positive correlations between current and desired weight and current and desired BMI for both genders. Those with a heavier current weight had a heavier desired weight (male: r⫽0.684, p⬍0.0005; female: r⫽0.750, p⬍0.0005); those with a higher BMI had a higher desired BMI (male: r⫽0.641, p⬍0.0005; female: r⫽0.630, p⬍0.0005). A similar correlation was found for current and desired body image (male: r⫽0.541, p⬍0.0005; female: r⫽0.728, p⬍0.0005). There were statistically significant associations between BMI category of the current and desired calculated BMI (male: 2⫽14.466, p⬍0.0005), and BMI categories of the current and desired images (male: 2⫽9.817, p⬍0.002; female Fisher’s exact p⫽0.008). Conclusions/Applications: Data indicated that the heavier the individuals, the heavier the desired weight, and the larger the desired images. There was a positive association between the current weight and image, and the desired weight and image. Further research is needed focusing on body image and weight. Funding Disclosure: None
A-76 / September 2009 Suppl 3—Abstracts Volume 109 Number 9
RESEARCH & PRACTICE INNOVATIONS: TRANSLATING RESEARCH INTO DIETETICS PRACTICE (PART 2) Perceived Diffusion Attributes of Nutrition Education Curricula Author(s): A. Diker,1 L. M. Walters,2 L. Cunningham-Sabo1; 1Food Science and Human Nutrition, Colorado State University, Fort Collins, CO, 2Cooking with Kids, Inc., Santa Fe, NM Learning Outcome: The participant will be able to identify perceived attributes that affect the rate of adoption of nutrition education curricula. Background: Understanding perceived attributes of successful nutrition education curricula is useful in identifying appropriate curriculum dissemination strategies. Rogers’ Diffusion of Innovations Theory identifies five perceived attributes (compatibility, relative advantage, complexity, trialability, and observability) which affect diffusion and adoption rates. Results of a web-based survey were analyzed to identify perceived attributes contributing to nutrition education curricula adoption. Methods: Secondary data analysis was performed of survey results from 109 respondents who registered to download free fruit and vegetable nutrition education lessons. Construct validity of questionnaire items was assessed using principal axis factor analysis. Cronbach’s alpha was used to determine internal consistency reliability of questions based on factor analysis results. Predictors of intended future use of tasting lessons were assessed via stepwise multiple regression. Results: Factor analysis revealed the five perceived attributes accounted for 31.9% of cumulative variance. Sixteen questions with factor loading greater than 0.40 were retained from the initial set of 42 questions. Reliability testing based on factor loading resulted in adequate Cronbach’s alpha values for trialability (.611), compatibility (.613), complexity (.613), and relative advantage (.685). Stepwise multiple regression revealed trialability significantly predicted future planned usage of tasting lessons (p⫽0.006).
Using the USDA Multivitamin/Mineral Calculator to Apply Research from the Dietary Supplement Ingredient Database (DSID) to Assess Nutrient Intake Author(s): J. M. Roseland,1 K. W. Andrews,1 J. M. Holden,1 C. Zhao,1 A. Middleton,1 M. Feinberg,1 L. W. Douglass,2 J. T. Dwyer,3 M. F. Picciano,3 L. G. Saldanha,3 C. T. Sempos,3 R. Bailey3; 1Nutrient Data Laboratory, USDA/ ARS/BHNRC, Beltsville, MD, 2Consulting Statistician, Longmont, CO, 3Office of Dietary Supplements, NIH, Bethesda, MD Learning Outcome: Participants will be able to discuss how the MVM calculator applies research results to provide estimated vitamin and mineral levels in adult MVMs. Background: Complete information on nutrient intake from dietary supplements, as well as from foods, is necessary to evaluate diet-health relationships. To address this need, the Dietary Supplement Ingredient Database-Release One (DSID-1) was recently developed in collaboration between USDA and the NIH Office of Dietary Supplements. DSID-1 includes analytically-based regression predictions for ingredient levels in adult MVMs (defined as ⬎3 vitamins) and a multivitamin/mineral (MVM) calculator. The interactive calculator provides estimates and variability measures for 18 nutrients in adult MVMs when users enter labeled values. Methods: Nutrient estimates are based upon data from a nationwide study of representative adult MVMs conducted by USDA from 2006 to 2008. About 115 products were purchased in six locations from various market channels. Results of nutrient analyses at qualified laboratories compared mean percent difference between analyzed and labeled levels using regression analysis. Predicted values and standard errors of the mean for 18 nutrients were calculated. Results: Predicted mean difference from label at the most common level per nutrient ranged from 0 to 10% above label for 11 nutrients, 10 to 20% for 4 nutrients, and ⬎20% above label for 3 nutrients.
Conclusion: Trialability, the degree to which an innovation may be experimented with prior to adoption, likely is an important factor for adoption and implementation of nutrition education curricula. Programs should consider incorporating a trialability component, such as a free introductory lesson or curriculum pilot-testing by a small number of educators, to enhance curriculum adoption.
Conclusion: The calculator features user-friendly applications. Researchers and other professionals can employ the calculator or DSID-1 data files to acquire analytically-derived nutrient estimates for generic forms of adult MVMs, create electronic files, and combine supplement information with food data for total nutrient estimates. DSID-1 aids appraisal of nutrient intakes from adult MVMs, complementing the USDA National Nutrient Database for Standard Reference for foods.
Funding Disclosure: USDA CSREES NRI 2007-05062
Funding Disclosure: Office of Dietary Supplements/NIH
Body Image Self-Perception Author(s): C. L. Atwell,1 D. Rigassio Radler,2 J. Ziegler,2 R. Touger-Decker,3 H. Khan4; 1Norton Community Hospital, Norton, VA, 2Nutritional Sciences, University of Medicine and Dentistry of New Jersey, Newark, NJ, 3Nurtitional Sciences, University of Medicine and Dentistry, Newark, NJ, 4University of Medicine and Dentistry of New Jersey, Newark, NJ Learning Outcome: Dietitians and other health professionals, by knowing that some people desire what clinically is not a healthy weight, will be able to customize education to those individual’s specific needs. Objective: To determine relationships between self-reported and desired body weight, calculated current and desired body mass index (BMI), and perceived current and desired body image among University of Virginia at Wise (UVAWise) Freshmen students. Methods: A descriptive survey design was used with a cross-sectional convenience sample of students ⱖ18 years old (n⫽139). The self-administered anonymous, questionnaire included demographic data, self-reported height, weight, desired weight, and perceived current and desired body image. Descriptive statistics, Pearson correlation coefficients, and Chi-Square test were used. Results: Of the 139 questionnaires, 86% (n⫽120) were usable; 52.5% were male; mean age⫽18.43 (range⫽18-25 years). There were strong statistically significant positive correlations between current and desired weight and current and desired BMI for both genders. Those with a heavier current weight had a heavier desired weight (male: r⫽0.684, p⬍0.0005; female: r⫽0.750, p⬍0.0005); those with a higher BMI had a higher desired BMI (male: r⫽0.641, p⬍0.0005; female: r⫽0.630, p⬍0.0005). A similar correlation was found for current and desired body image (male: r⫽0.541, p⬍0.0005; female: r⫽0.728, p⬍0.0005). There were statistically significant associations between BMI category of the current and desired calculated BMI (male: 2⫽14.466, p⬍0.0005), and BMI categories of the current and desired images (male: 2⫽9.817, p⬍0.002; female Fisher’s exact p⫽0.008). Conclusions/Applications: Data indicated that the heavier the individuals, the heavier the desired weight, and the larger the desired images. There was a positive association between the current weight and image, and the desired weight and image. Further research is needed focusing on body image and weight. Funding Disclosure: None
A-76 / September 2009 Suppl 3—Abstracts Volume 109 Number 9