Article

Need for Technological Innovation in Dietary Assessment

National Cancer Institute, Division of Cancer Control and Population Sciences, Applied Research Program, Risk Factor Monitoring and Methods Branch, Bethesda, MD 20892-7344, USA.
Journal of the American Dietetic Association (Impact Factor: 3.92). 01/2010; 110(1):48-51. DOI: 10.1016/j.jada.2009.10.008
Source: PubMed
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    ABSTRACT: Dietary intake assessment with diet records (DR) is a standard research and practice tool in nutrition. Manual entry and analysis of DR is time-consuming and expensive. New electronic tools for diet entry by clients and research participants may reduce the cost and effort of nutrient intake estimation. To determine the validity of electronic diet recording, we compared responses to 3-day DR kept by Tap & Track software for the Apple iPod Touch and records kept on the Nutrihand website to DR coded and analyzed by a research dietitian into a customized US Department of Agriculture (USDA) nutrient analysis program, entitled GRAND (Grand Forks Research Analysis of Nutrient Data). Adult participants (n=19) enrolled in a crossover-designed clinical trial. During each of two washout periods, participants kept a written 3-day DR. In addition, they were randomly assigned to enter their DR in a Web-based dietary analysis program (Nutrihand) or a handheld electronic device (Tap & Track). They completed an additional 3-day DR and the alternate electronic diet recording methods during the second washout. Entries resulted in 228 daily diet records or 12 for each of 19 participants. Means of nutrient intake were calculated for each method. Concordance of the intake estimates were determined by Bland-Altman plots. Coefficients of determination (R(2)) were calculated for each comparison to assess the strength of the linear relationship between methods. No significant differences were observed between the mean nutrient values for energy, carbohydrate, protein, fat, saturated fatty acids, total fiber, or sodium between the recorded DR analyzed in GRAND and either Nutrihand or Tap & Track, or for total sugars comparing GRAND and Tap & Track. Reported values for total sugars were significantly reduced (P<.05) comparing Nutrihand to GRAND. Coefficients of determination (R(2)) for Nutrihand and Tap & Track compared to DR entries into GRAND, respectively, were energy .56, .01; carbohydrate .58, .08; total fiber .65, .37; sugar .78, .41; protein .44, .03; fat .36, .03; saturated fatty acids .23, .03; sodium .20, .00; and for Nutrihand only for cholesterol .88; vitamin A .02; vitamin C .37; calcium .05; and iron .77. Bland-Altman analysis demonstrates high variability in individual responses for both electronic capture programs with higher 95% limits of agreement for dietary intake recorded on Tap & Track. In comparison to dietitian-entered 3-day DR, electronic methods resulted in no significant difference in mean nutrient estimates but exhibited larger variability, particularly the Tap & Track program. However, electronic DR provided mean estimates of energy, macronutrients, and some micronutrients, which approximated those of the dietitian-analyzed DR and may be appropriate for dietary monitoring of groups. Electronic diet assessment methods have the potential to reduce the cost and burden of DR analysis for nutrition research and clinical practice. Clinicaltrials.gov NCT01183520; http://clinicaltrials.gov/ct2/show/NCT01183520 (Archived by WebCite at http://www.webcitation.org/6VSdYznKX).
    Journal of Medical Internet Research 01/2015; 17(1):e21. DOI:10.2196/jmir.3744 · 4.67 Impact Factor
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    ABSTRACT: In this paper we describe an image segmentation method for segmenting food items in images used for dietary assessment. Dietary assessment methods used to determine the foods and beverages consumed at a meal are essential for understanding the link between diet and health. Snakes, or active contours, are used extensively to locate object boundaries and segment images. Experimental results using classical snakes on food images show the problems associated with contour initialization and poor detection performance for food images. In this paper, we explore various methods of contour initialization and integrate a background removal method to improve the performance of food image segmentation. We describe the details of the proposed food image segmentation method and also evaluate our segmentation approach on food images.
    2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP); 09/2012
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    ABSTRACT: Dietary assessment can be challenging for many reasons, including the wide variety of foods, eating patterns and nutrients to be considered. In team-based primary care practice, various disciplines may be involved in assessing diet. Electronic-based dietary assessment (e-DA) instruments available now through mobile apps or websites can potentially facilitate dietary assessment. Providers views of facilitators and barriers related to e-DA instruments and their recommendations for improvement can inform the further development of these tools. The objective of this study was to explore provider perspectives on e-DA tools in mobile apps and websites. The exploratory sequential mixed methods design included interdisciplinary focus groups followed by a web-based survey sent to Family Health Teams throughout Ontario, Canada. Descriptive and bivariate analyses were completed. Focus group transcripts contributed to web-survey content, while interpretive themes added depth and context. 11 focus groups with 50 providers revealed varying perspectives on the use of e-DA for: 1) improving patients' eating habits; 2) improving the quality of dietary assessment; and, 3) integrating e-DA into the care process. In the web-survey 191 respondents from nine disciplines in 73 FHTs completed the survey. Dietitians reported greater use of e-DA than other providers (63% vs.19%; p = .000) respectively. There was strong interest among disciplines in the use of e-DA tools for the management of obesity, diabetes and heart disease, especially for patient self-monitoring. Barriers identified were: patients' lack of comfort with using technology, misinterpretation of e-DA results by patients, time and education for providers to interpret results, and time for providers to offer counselling. e-DA tools in mobile apps and websites may improve dietary counselling over time. Addressing the identified facilitators and barriers can potentially promote the uptake of e-DA into clinical practice.
    BMC Medical Informatics and Decision Making 12/2015; 15(1):138. DOI:10.1186/s12911-015-0138-6 · 1.50 Impact Factor

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